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Toward an information management system for handling parenting information users' comments

2015· article· en· W2282787636 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicFocus Groups and Qualitative Methods
Canadian institutionsLucie and André Chagnon FoundationMcGill University
Fundersnot available
KeywordsCoding (social sciences)Computer scienceGeneral partnershipQualitative researchKnowledge managementPsychologyBusiness

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.014
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.358
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it