Bibliographic record
Abstract
Since those early days when students worked without scanners and without a sufficiently detailed data entry protocol, we have come a long way.In March, 2015, the last card was digitized.This, however, is not the end of the project.The eventual goal is to make the word-file collection available to interested researchers as it contains a wealth of data on local lexicon, pronunciation and grammar, in addition to many notes between editors that shed light on the process and methods of writing a dictionary.Though the collection is now digitized, a database remains to be built and made available.We are in consultations with Memorial's Digital Archives Initiative, Information Technology Services and Heritage NL to determine the best platform on which to launch this valuable culturally-rich corpus.A total of 68 students have worked on the project since its 2005 inception.While we are happy to have offered research and archival experience to so many undergraduate and graduate students, this has inevitably introduced a good deal of variation and error into the treatment of the cards and the information they contain.As with any methodologically sound social sciences or humanities research project, a second verification phase is required in order to ensure uniform data entry and to make the resulting database as functional, searchable and error-free as possible.This second phase was started in mid-2014.At the date of writing, 10 drawers have been verified and 5 drawers have been partially verified, out of a total of 72.It is anticipated that database creation and mounting of the word-files will occur in tandem with the verification stage.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".