Seeking information for a middle school history project: The concept of implicit knowledge in the students' transition from<scp>K</scp>uhlthau's<scp>S</scp>tage 3 to<scp>S</scp>tage 4
Bibliographic record
Abstract
The article reports the findings of a content analysis study of 16 student‐group proposals for a grade eight history project. The students listed their topic and thesis in the proposal, and information in support of their thesis. The study's focus is this topic‐to‐thesis transition. The study's conceptual framework is Kuhlthau's six stageISPModel's transition from exploring information inStage 3 to formulating a focus or personal perspective on the assignment topic inStage 4. Our study coding scheme identifies elements of the students' implicit knowledge in the 16 proposals. To validate implicit knowledge as a predictor of successful student performance, implicit knowledge was coded, scored, and then the correlation coefficient was established between the score and the students' instructors' marks. InPart 2 of the study we found strong and significant association between theMcGill coding scores and the instructors' marks for the 16 proposals. This study is a first step in identifying, operationalizing, and testing user‐centered implicit knowledge elements for future implementation in interactive information systems designed for middle school students researching a thesis‐objective history assignment.
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.
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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".