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IAM

Identity and Access Management (IAM) is a framework comprising policies, processes, and technologies for administering digital identities throughout their lifecycle and enforcing access controls to organizational resources, ensuring that users have appropriate permissions based on verified identity and context. This includes core functions such as identity proofing, credential issuance, (e.g., via passwords, , or multi-factor methods), authorization via models like , and ongoing monitoring to revoke access when necessary. As a foundational element of cybersecurity, IAM supports with standards like NIST SP 800-63 for guidelines and ISO/IEC 27001 for , mitigating risks from unauthorized access, insider threats, and credential compromise in distributed environments such as . Its evolution has emphasized principles like least privilege—granting minimal necessary access—and zero trust, which assumes no implicit trust regardless of location, driven by rising incidents of identity-based attacks that for over 80% of breaches according to federal analyses. Notable advancements include systems for across domains and integration with emerging technologies like for decentralized credentials, though challenges persist in balancing with , particularly in scaling for remote workforces and third-party integrations. Poorly implemented IAM has been implicated in major vulnerabilities, highlighting the need for automated provisioning, regular audits, and to maintain efficacy.

Science and Technology

Identity and Access Management

Identity and access management (IAM) refers to the processes and technologies used to administer individual identities within an organizational system, network, or broader ecosystem, ensuring that only verified users gain appropriate access to resources. It involves identifying users, authenticating their credentials, authorizing their permissions based on roles or attributes, and maintaining accountability through logging and auditing. IAM frameworks integrate policies, tools, and procedures to manage digital identities securely, preventing unauthorized access while enabling efficient operations. Core components of IAM include , which verifies user identity through methods like passwords, , or (MFA); , which determines what actions a verified user can perform; and provisioning, which handles the creation, modification, and deactivation of user accounts across systems. Additional elements encompass (SSO) for seamless access across applications, (RBAC) to assign permissions by job function, and federated identity management for cross-organization trust. These components collectively form a layered defense, often aligned with the , , and accounting (AAA) model standardized in protocols like and . In cybersecurity, IAM serves as a foundational by enforcing the principle of least privilege, where users receive only the minimum access necessary for their roles, thereby reducing the from credential compromise or threats. Effective IAM mitigates risks such as data breaches, with studies indicating that weak controls contribute to over 80% of cybersecurity incidents involving compromised credentials. It supports with frameworks like GDPR and HIPAA by providing audit trails and access revocation capabilities, while adaptive controls—using contextual factors like location or device—enhance real-time threat detection. Standards from bodies like NIST guide IAM implementation, with NIST Special Publication 800-63 outlining guidelines for assurance levels (AALs) ranging from low-risk single-factor to high-risk multi-factor with hardware tokens. The (CSF) 2.0 integrates IAM into its Identify, Protect, and Detect functions, emphasizing governance and . Internationally, ISO/IEC 27001 incorporates IAM within systems, focusing on controls for user registration, privilege management, and review processes. These standards promote and best practices, such as zero-trust architectures that verify every access request regardless of origin. IAM has evolved from basic username-password systems in early computing to sophisticated, cloud-native solutions driven by the proliferation of and environments since the early . NIST's involvement dates back over 50 years, with foundational work on personal identity verification () cards standardized in the for federal systems. Modern advancements include and AI-driven , addressing challenges like scalability in zero-trust models and the management of non-human identities such as and devices. Despite these, persistent issues include over-provisioning of privileges and integration complexities in legacy systems, underscoring the need for continuous auditing and policy enforcement.

Integrated Assessment Modeling

Integrated assessment models () are computational frameworks that simulate interactions among economic, energy, , and systems to evaluate long-term scenarios related to environmental challenges, particularly anthropogenic change. These models integrate simplified representations of physical processes, such as and , with socioeconomic dynamics like and consumption patterns, often producing cost-benefit analyses for strategies. emerged in the late and early , building on earlier energy-economy models from the oil crises, with foundational contributions from economists like , whose Dynamic Integrated Climate-Economy () model, introduced in 1992, linked macroeconomic growth to carbon damages via quadratic damage functions. IAMs typically fall into two categories: reduced-form models, which use aggregated equations to approximate climate and economic feedbacks without detailed sectoral resolution, such as DICE, PAGE, and FUND; and process-based models, which incorporate granular simulations of energy systems, agriculture, and emissions pathways, exemplified by IMAGE, MESSAGE, and REMIND. These models rely on assumptions about discount rates (often 1-5% annually), elasticity of substitution in production functions, and damage functions that scale economic losses quadratically with temperature rise, typically estimating global GDP reductions of 1-3% under 2-3°C warming scenarios by 2100. For instance, the DICE model's 2016 revision projected a social cost of carbon (SCC) of approximately $40 per ton of CO2 in 2020 dollars under a 3% discount rate, influencing regulatory analyses. In policy applications, IAMs have informed (IPCC) assessments since the 1990s, generating scenarios for the Representative Concentration Pathways (RCPs) and (SSPs), and contributing disproportionately to IPCC Summary for Policymakers sections on mitigation feasibility, with IAM-derived content appearing in 20-30% of such summaries across six assessment cycles through 2023. They underpin estimates of the SCC, used by agencies like the U.S. Environmental Protection Agency for valuing emissions reductions, though variations across models yield SCC ranges from $10 to $100 per ton, reflecting differences in and damage parameterization. IAM outputs have supported arguments for carbon pricing over abrupt transitions, as in the 2018 IPCC Special Report on 1.5°C, where models showed net benefits from gradual decarbonization via negative emissions technologies like bioenergy with (BECCS). Criticisms of IAMs center on their underestimation of climate risks due to omitted non-linearities, such as tipping points (e.g., thaw or collapse), which empirical paleoclimate data suggest could amplify damages beyond quadratic functions, potentially increasing SCC by factors of 2-10. Models often assume high substitutability between capital and energy, optimistic technological deployment (e.g., 100-600 GtCO2 of BECCS by 2100), and , neglecting path dependencies, political inertia, and fat-tailed uncertainty in temperature responses, as evidenced by discrepancies between IAM physics modules and standalone models like CMIP6, where IAMs project 0.5-1°C lower warming for equivalent emissions. Furthermore, IAMs frequently undervalue non-market damages, such as or health impacts, comprising less than 10% of total assessed losses in models like FUND, and exhibit structural biases toward developed economies' preferences, with limited representation of intragenerational inequities despite calls for justice-integrated frameworks. These limitations, rooted in data scarcity and computational constraints, have prompted calls for approaches incorporating agent-based modeling or empirical validation, though IAM developers counter that such simplifications enable tractable insights absent perfect .

Organizations

International Association of Machinists and Aerospace Workers

The International Association of Machinists and Workers (IAMAW), commonly known as the IAM, was founded on October 8, 1888, in , , by 19 machinists who met in a locomotive pit to address low wages of 20-25 cents per hour for 10-hour days and poor working conditions in the railroad industry. Initially organized as the United Machinists and Mechanical Engineers of America, the group held its first convention in 1889, electing Tom Talbot as Grand Master Machinist and launching a monthly to build . By 1890, it adopted the name International Association of Machinists upon chartering its first Canadian local, reaching 4,000 members, and affiliated with the in 1895. The IAM expanded through aggressive organizing and strikes, achieving an 8-hour day in many shops by 1915 and peaking at 331,000 members in 1918 amid demands for production. A major 1922 railroad strike involving 79,000 members led to membership dropping to 148,000 due to government intervention and employer resistance, but recovery followed with diversification into . In 1964, the name changed to International Association of Machinists and Workers to reflect growth in and sectors, with membership at 800,000; it surpassed 1 million by 1968. Key milestones include the 1966 strike against five major airlines that broke a 3.2% cap, and mergers like the 1988 integration of the Industrial Union of and Workers during its centennial. Structured as a federation of over 2,000 local lodges grouped into districts, the IAM operates from its headquarters in , representing workers in , , transportation, defense, and related fields, including major employers like , , and . As of 2025, it claims nearly 600,000 active and retired members across , focusing on for wages, job security, and benefits. Current leadership includes International President Brian Bryant, a former elected in 2024, and General Secretary-Treasurer Dora Cervantes. The IAM has a history of high-profile labor actions, such as the 2024 Boeing strike involving over 30,000 members demanding inflation-adjusted raises and pension restoration, which disrupted production and highlighted tensions over and subcontracting. Affiliated with the , the union engages in political advocacy through voluntary member contributions via its Machinists Non-Partisan Political League, endorsing Democratic candidates like in 2024 after internal member votes, reflecting its pro-labor priorities amid criticisms of one-party alignment.

Independent Arts & Media

Independent Arts & Media (IAM) is a San Francisco-based 501(c)(3) founded in 2000, serving as a fiscal sponsor for independent artists, journalists, and media producers engaged in non-commercial, public-interest projects. The organization supports over 60 affiliate projects, providing administrative services, grant administration, and tax-exempt status to enable creators to focus on work that promotes and civic participation. IAM's mission emphasizes stewarding projects that foster diverse voices and free expression as cultural necessities, particularly through for initiatives in online and print , theater, , and . Initially focused on building accessible and affordable media and arts services for producers in the , IAM expanded to include partnerships with other arts service organizations, such as contributing to the Bay Area Arts Worker Relief Funds during economic challenges. The organization operates from 2830 20th Street, Suite 201, in , and maintains an annual economic statement to ensure transparency in its model. IAM does not engage in commercial activities but prioritizes non-profit endeavors that align with , distinguishing it from for-profit entities. Supporters include foundations like the Bently Foundation, which has granted funds to IAM for its umbrella services to small art projects seeking nonprofit status.

Arts and Entertainment

IAM (French Hip-Hop Group)

IAM is a French hip-hop collective originating from Marseille, formed in 1989 by core members Akhenaton (real name Philippe Fragione, rapper and producer), Shurik'n (Geoffroy Mussard, rapper), DJ Khéops (Eric Mazel, turntablist), Imhotep (Pascal Perez, DJ and producer), and Kephren (François Mendy, beats and sampling). The group emerged from earlier Marseille-based crews like B-Boys Stance, drawing inspiration from American acts such as Public Enemy and Wu-Tang Clan, and adopted the name IAM as an acronym for "Imperial American Hip Hop" before evolving into broader thematic references. Pioneering French rap outside the Paris scene, IAM emphasized dense lyricism, social commentary on urban life, immigration, and global politics, blended with intricate production incorporating jazz, funk, and Egyptian motifs reflective of Akhenaton's interests. The group's debut album, De la planète Mars, released on October 21, 1991, via , sold over 100,000 copies and established them as trailblazers, being the first full-length rap album by a non-Parisian act to achieve commercial success. Follow-up Ombre est lumière (1993) expanded their reach, but L'École du micro d'argent (March 24, 1997) solidified their legacy, peaking at number one on charts, earning for over 1 million units sold, and featuring tracks like "L'École du micro d'argent" and "Demain, c'est loin" that critiqued and societal ills through multilingual and philosophical depth. Later works include Revoir un printemps (2003), IAM Concept compilations, and (2022), maintaining their influence amid lineup changes, such as Freeman's addition in the and departure in 2009. IAM has sold millions of globally, contributed to soundtracks like (1998), and performed internationally, including a 2024 Central Park concert, underscoring their role in globalizing .

Other Uses

IAM in Linguistics and Philosophy

The iamitive, abbreviated as IAM in linguistic glossing conventions, refers to a of markers that encode anteriority or completion of an event with an associated change of state, often implying "already" and a contrast to prior expectation or situation. The term derives from the Latin adverb iam ("already" or "now") and was proposed in typological to distinguish such markers from traditional perfect aspects, which emphasize ongoing relevance rather than state transition. First systematically explored in the early , iamitives highlight a semantic focus on irreversible shifts, such as in examples where an event's completion unexpectedly resolves a pending condition. In cross-linguistic surveys, iamitive-like markers appear in languages of (e.g., sudah, functioning beyond mere "already" to signal resultant states), the (e.g., grammaticalized forms denoting completed shifts), and Austronesian families, where they grammaticalize from lexical sources like "finish" or "arrive." Typologists such as Östen Dahl and Bernhard Wälchli argue that iamitives occupy a distinct region in the grammatical space of aspectual categories, overlapping with resultatives but differentiated by their discourse-oriented of counterexpectation or novelty. For instance, in Bunun (an Austronesian language), certain particles serve as discourse markers aligning with iamitive semantics, integrating temporal anteriority with pragmatic surprise. Debates persist on the category's , with critics contending that iamitives lack a precise and may subsume under broader perfect or mirative functions, potentially blurring boundaries in languages without dedicated forms. Empirical studies, including analyses of negated contexts and scalar implicatures, support iamitives as evaluative particles sensitive to and focality, as seen in Latin iam analogs extended to modern . In , iamitives inform inquiries into how languages conceptualize temporal asymmetry and causal change, challenging universalist assumptions in formal semantics by evidencing culture-specific encodings of event completion and violation. This intersects with debates on aspectuality, where markers like iamitives reveal non-compositional meanings tied to of time, as opposed to purely logical structures.

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