D5.6 First report on the evaluation process
Bibliographic record
Abstract
Within the PEER project, the WP5, entitled “Learn: Piloting and Evaluation” presents the detailed usage scenarios, integrates the components of the PEER project in the use-cases, and obtain feedback from users. WP5 contains Task T5.7 Management of the Evaluation Process, related to every aspect of the evaluation process.CATIE is responsible for T5.7 “Management of the Evaluation Process”, starting in M10 and finishing in M48. This task encompasses the development of an evaluation protocol of acceptance levels for each AI (Artificial Intelligence) prototype from the project, the actual performance of the evaluation and the centralization of the results to present before the consortium. This sequence will be performed 3 times during the project, improving the prototypes from TRL 3 (Technical Readiness Level) to TRL 5. Additionally, this task encompasses the testing of the 1st version of AIA (Artificial Intelligence Acceptance) index to be developed in WP4, for which CATIE is also responsible. These data will be presented extensively in the deliverable D4.2 which is due in M36.The present deliverable D5.6 Report on the Evaluation Process presents the methodology, the results and perspectives on the evaluation of the MVP (Minimal Viable Product) for each use-case. This evaluation will be performed 3 times iteratively to improve the prototypes, from partially simulated (TRL 3, Year 2) to functional in real conditions (TRL 5, Year 4) prototypes.The results related to the use of MVPs revealed that participants reported sufficient comprehensibility from the prototype to achieve the usage scenario. Noteworthy, all participants but one was able to complete the task. However, most participants reported a limited understanding of the overall functioning of the prototypes.We collected feedback specific to each use-case and elements to improve each prototype as well. The main improvement avenue is related to the timely provided explanations by the AI assistant.We also collected feedback on the first version of the AIA Index. The results related to the AIA index were crucial to improve it for the next evaluation. The constructs focused on in the PEER project – namely comprehensibility, user agency, overall trustworthiness – exhibited satisfactory results given the elements collected through the corresponding items. We were able to identify avenues for improvements, especially on social-oriented constructs, such as cultural aspects, ethics, privacy and data governance.
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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 | 0.100 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.111 | 0.125 |
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".