Student Reviewer Training for Western Undergraduate Research Journal: Health and Natural Sciences
Bibliographic record
Abstract
Conducting workshops on effective scientific writing Conducting workshops on finding summer research opportunities Developing new and innovative approaches to involve undergraduate students in scholarly workRead before you commit to the review • Read the abstract before and after the paper • Look for -A concise summary of the paper including a statement of intent and conclusions -Does the information in the abstract coincide with information in the paper?-Is the abstract merely a "cut and paste" from the major sections? DISCUSSION NO NEW MATERIAL! NO SURPRISES!A look at references• How old are the references?• Are there important papers missing?After the initial perusal… Read the entire paper, then write a summary.Your summary should be as concise, if not more concise, than the author's Write a paragraph about what's good about the paper (not always necessary) Major comments: Write about the assumptions, approach, analysis, results, conclusions, references.Suggest ways to improve where improvement is necessary.The process (continued)
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 | 0.069 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.198 | 0.111 |
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".