Espoused Theories and Theories-In-Use of Information Literacy
Bibliographic record
Abstract
What values, beliefs and conceptions (espoused theories) underpin and shape professional practice (theories-in-use) in information literacy education? This study investigates relationships between espoused theories and theories-in-use of information literacy in academic libraries. The paper reports preliminary findings from an in-depth comparative analysis of one library’s official policy documents and its instruction resources including an online research tutorial. The findings indicate varying patterns of congruence and incongruence between the library’s espoused theories and theories-in-use with incidents of significant gaps. The process of examining espoused theories and theories-in-use provides an evaluative framework for critically analyzing practice with the view of aligning practice more closely with stated goals and rhetoric. The study is therefore presented as a practical method for evaluating tools of information literacy practice in the school library.
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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 | 0.051 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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, 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".