Bibliographic record
Abstract
There are several building blocks of defining a well-performing Voila quelques-unes des pierres d'assise de la dkfintion d'une organization like the CIF/IFC.Two of these are relevancy to organisation bien portante c o m e L'Institut.Deux de ces 616the members and partnering.ments touchent les membres et le partenariat.The first is strength in membership, not just numbers, but Le premier ClCment rkside dans la force des membres, non those who define the value of the organization by their own pas seulement le nombre de membres mais ceux qui dtfinispersonal actions and participation in the sent la valeur de I'organisation par leurs proaffairs and aspirations of promoting the pres initiatives personnelles et leur particorganization's vision and mission.Over the ipation dans les affaires et leurs dCsirs de past few years, steps have been taken to promouvoir la vision et la mission de involve the membership in the affairs of I'organisation.Au cours des quelques the Institute.For example, directors are dernikres annks, des Ctapes ont ttC franchies more participatory, communications have pour impliqu~r les membres dans les affaires improved through daily e-mail and Internet de 171nstitut.A titre d'exemple, les directeurs services, and standing committees and work-sont plus impliquts, les communications se ing groups are working on the issues and polisont amtliortes par I'entremise de courriels cies affecting our profession.Further, we are quotidiens et de services Internet, et les developing long-term vision with Strate-comitts permanents ainsi que les groupes gic, Communications, Membership and de travail kflekhissent aux enjeux et aux poli-Business Plans.tiques qui affectent notre profession.De plus, The second building block is developing
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.016 |
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".