Deep, efficient, and dialogic study of undergraduate information seeking and use: A methodological exploration
Bibliographic record
Abstract
Abstract Our purpose is to present the rationale and results of a pilot study applying a meta‐theoretically derived approach to focus group or semi‐structured group interviewing as a means of obtaining deep data efficiently and dialogically. The project was designed to meet needs of both the senior author (a methodologist) and junior author (a librarian). The exemplar was undergraduate information seeking and use for meeting class assignments. Particular focus was on understanding when and why students decided between quick internet‐only versus thorough searches involving multiple sources. The focus group approach was informed by Dervin's Sense‐Making Methodology (SMM). Emphasis for this presentation is methodological – to describe the approach and to illustrate the potentials for group interviewing. Empirical results provided here are for illustration only. The pertinent literature review emphasizes methodological sources. The authors are collecting more data from a range of different academic informants and expect to develop empirically anchored reports in the near future.
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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 | 0.142 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".