Bibliographic record
Abstract
document shows the formatting requirements for UBC theses. Theses must follow these guidelines in order to be accepted at the Faculty of Graduate Studies for publication in the UBC Library and in Library and Archives Canada. Please use this document to review your thesis formatting, as it will alert you to some common errors and omissions. This document uses the term "thesis " to mean either a thesis or a doctoral dissertation. The formatting requirements are the same for both. IMPORTANT! You do not have to use the same font, chapter numbering and general style of this training document for your thesis. Please consult with your program and follow a style guide for your discipline. Font size for text should be 10- 12 point if you are using Arial or Times New Roman. If you are using another font, please ensure that it is no smaller than these two examples. The idea for this guide came from the work of Penny Simpson, Assistant for Theses, SFU Library. Many thanks! Review Your Thesis or Dissertation May 2010Avoid using scientific symbols or Greek letters in your title; spell out the words. Must be lower case.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.938 | 0.957 |
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".