THE INTERNATIONAL NETWORK OF RESEARCH MANAGEMENT SOCIETIES
Bibliographic record
Abstract
About INORMS: INORMS, formed in 2001, serves as a collaborative platform bringing together research management societies and associations globally. Its primary goal is to facilitate interactions, sharing of best practices, and joint activities among member societies, ultimately benefiting their individual members. Despite having distinct remits, constitutions, memberships, and geographical bases, member societies face similar challenges in the evolving landscape of research management. INORMS Objectives: Members: INORMS currently comprises 21 research management societies from around the world, including notable ones like the Association of Research Managers and Administrators (UK), Australasian Research Management Society, and Society of Research Administrators International. Membership: Research management societies are encouraged to join INORMS to foster the development of similar associations in under-represented areas. Benefits of Membership: INORMS membership offers advantages such as knowledge exchange on international best practices, skill development through resource sharing, and the establishment of professional networks across global sectors involved in research management. JOIN INORMS: The process for societies to become members involves completing and returning the INORMS Membership Registration Form. Applications are considered by the INORMS Council, with a short presentation required from applicants. INORMS Governance: The INORMS Council provides leadership and is comprised of the Chairs/Presidents of member associations. The council is underpinned by a set of Operating Principles reviewed annually. A Working Group was formed in 2017 to strengthen the network's value proposition, progressing ideas and joint initiatives for the benefit of the network. Research Impact and Stakeholder Engagement Working Group: Co-chaired by David Phipps (York University, Canada) and Julie Bayley (University of Lincoln, UK), this group focuses on building the capacity of research managers and administrators to support researchers in maximizing the impacts of their work. The RISE Working Group, which concluded in Autumn 2020, developed criteria to assess products and services supporting research impact. Research Administration as a Profession (RAAAP) Taskforce: The RAAAP survey identifies key skills, attitudes, and behaviors of successful research management and administration leaders. The taskforce, established in 2016, aims to continue surveying Research Managers and Administrators every three years, collecting and analyzing longitudinal data about the profession. INORMS Research Evaluation Group: Previously known as REWG, this group, established in 2018, focuses on ensuring meaningful, responsible, and effective research evaluation. It developed the SCOPE framework and 'five arguments' for engaging senior leaders in responsible research evaluation. The group also addresses the influence of Global University Rankings on university behavior and introduced the More Than Our Rank initiative to reconsider their utilization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.130 | 0.106 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".