Bibliographic record
Abstract
There are many styles that can be used for referencing. When you are given coursework or dissertation guidelines, check which style of referencing your lecturer or department asks you to use. If you don't check, and you use a style that is not the one stated in your guidelines, you could find you lose marks. This guide introduces you to the Vancouver referencing style, which uses a 'numerical-endnote' approach. If your lecturer or department does not ask you to use any particular style, we recommend using Harvard. It's easy to learn, simple to use, and lots of advice is available to help you out. When you begin your research for any piece of work, it is essential that you record the details of all the information you find. You will need these details to provide accurate references, and to enable you to locate the information again later, should it be necessary to do so. Section 6 of this guide will help you identify what information you need, regardless of which referencing style you choose to use.
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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.019 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.166 | 0.133 |
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".