An Exploratory Study into the Application and Interpretation of Frequency Tables Relative to a Search Retrieval Set
Bibliographic record
Abstract
From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.Hidreth (1989:22) observed that subject searchers likely require a more interactive subject searching approach which allows for term and document appraisal and relevance feedback during the search process. In order to assess aspects of a user's evaluative skills, and of relevance feedback, a laboratory test was initiated to examine: 1) a procedure for evaluating a retrieved set: 2) a mechanism - a frequency table - to be used for that evaluation; and 3) the relationship between the evaluation procedure and the relevance feedback mechanism. Contrary to expectations that participants would use the frequency distribution tables to assist in evaluating a retrieved set, tables were used, instead, to modify the subsequent search strategy, to restructure the query. This suggests that frequency distribution tables may have some utility or value as interactive navigational aids to searchers.
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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 | 0.025 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".