Electronic thesis initiative: pilot project of McGill University, Montreal
Bibliographic record
Abstract
Purpose To set up a protocol for electronic thesis and dissertation (ETD) submission for the electronic thesis initiative pilot project at McGill University in Montreal, Canada. Design/methodology/approach An electronic thesis and dissertation submission protocol was implemented and tested. To test authoring tools, we had 50 students submit their theses or dissertations using one of four style sheets. Word‐processed files were converted to PDF and XML formats. The pilot project team evaluated DigiTool's effectiveness in digital conversion, capture of metadata and cataloguing, digital content harvesting, digital preservation, and integration with the student information system. Findings All theses experienced some degree of information loss during the conversion. DigiTool is still being tested for storage, cataloguing, and dissemination capability. For full implementation, three major issues need to be addressed further: conversion; metadata; and file formats. Practical implications Most of the issues that have arisen during the McGill pilot project will be mirrored at other academic institutions that are considering electronic thesis submission. Originality/value This paper provides insights into the procedures that will arise as institutions go through the process of introducing electronic thesis and dissertation submission.
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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 | 0.045 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".