Theory Building for the Profession of Teacher Librarianship: An Application of Meta-Ethnography
Bibliographic record
Abstract
The lack of theoretical foundations for research in the field of library and information science is well documented and leads researchers to borrow from other fields. In the case of school library research, the tendency is to borrow from the field of education. While many theories from education and other disciplines are applicable to school library research, there needs to be development of theory among school library researchers in order to give practitioners understanding of the complex relationships involved in school libraries and guide future research efforts. Meta-ethnography uses findings reported in previous studies as building blocks for gaining deeper understanding of a particular phenomenon and is highly applicable for the initial stages of theory development. This paper provides a thorough description of the meta-ethnography method and an example of how meta-ethnography can be applied for theory building in school library research.
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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 | 0.143 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".