Toward an information management system for handling parenting information users' comments
Bibliographic record
Abstract
ABSTRACT Little is known about how their qualitative feedback can be used by information providers. In this study, researchers worked with information providers, ‘Naitre et grandir’ (N&G), to implement the Information Assessment Method (IAM) for assessing and improving parenting information. Qualitative feedback was collected from participants who visited the N&G website during the study period and who completed an IAM questionnaire. Using an Organizational Participatory Research approach, a coding manual for the identification of participants’ comments was created, and developed by the researchers in partnership with information providers. This manual was used by two coders for classifying participants’ comments. A 4‐step process was followed. For each step, a sample of comments were codes, coding was compared, and codes were further refined. At step‐4, the inter‐coder reliability was tested. This led to a reliable coding manual that will be used in the creation of an online system to facilitate the coding of comments, and provide selected comments to N&G editors on a weekly basis. This system can be adapted by other website editors.
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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 | 0.172 | 0.281 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".