Helping Students Help ThemselvesReview of three resources: How to Get the Most Out of Studying: A Video Series; ChewS. L.; ( 2011). http://www.samford.edu/how-to-study/default.aspx?id=45097158404 (accessed August 10, 2012). Study Smarter, Not Harder: Use the Genius in You, 3rd ed.; PaulKevin; ( 2009). Self-Counsel Press, North Vancouver, BC. 224 pages How to Study Science, 4th ed.; DrewesFrederick W.MilliganKristin L. D.; ( 2002). McGraw Hill, New York, NY. 128 pages.
Bibliographic record
Abstract
Review of:Three tools that aim to teach students more effective study habits:How to Get the Most Out of Studying: A Video Seriesby S.L. Chew,Study Smarter, Not Harder: Use the Genius in You, 3rd Editionby Kevin Paul, andHow to Study Science 4th Editionby Frederick W. Drewes and Kristin L.D. Milligan.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.146 | 0.082 |
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".