A model for assessment and development of the credibility of Iran Academy of Medical Sciences
Bibliographic record
Abstract
Background & Aim: Assessment could be assumed as a valuable mean of highlighting the organization strengths and spotting its weaknesses. Academies are not exceptions in this regard. Knowing the items, which entail more concentrated attention, the leadership of the academy will shift the resources to compensate the extenuations. This study aimed to provide the Iran Academy of Medical Sciences (AMS) a model of assessment and development of its credibility. Methods & Materials: Reviewing the scientific literatures about the components of credibility of an organization, three components were elected, 1. Structure, 2. Performance, and 3. Acceptability. Assessing this academy, a framework for summarizing the information of other academies was developed. For the next steps, to improve the quality of the framework and to study more AMS, we decided to search the internet for more countries and academies. Results: We find that 16 indices and their 77 measures could be used to assess the AMS. Conclusion: Establishing a well-defined system with a trained staff devoted to assess the AMS activities, would be in the favor of evaluating the AMS annually; and by publication of strategic reports, AMS strengths would be reinforced and its weaknesses would be reformed.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| opus | Metaresearch Domain: Evaluation · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".