Building New Bridges - Bâtir de nouveaux ponts : Sources, Methods and Interdisciplinarity - Sources, méthodes et interdisciplinarité
Bibliographic record
Abstract
Questions of methodology and the use of sources are fundamental to all academic disciplines. In recent years, this topic has become far more challenging as scholars are increasingly adopting an interdisciplinary approach to achieve richer and deeper analyses, particularly in the humanities and social sciences. Building New Bridges / Bâtir de nouveaux ponts is a collection of scholarly papers that deals with the first principles of source identification and their effective utilization.The contributors to the volume come from a wide range of disciplines and represent both French and English Canada. Together, they explore and encourage the interdisciplinarity trend - around which considerable academic trepidation remains - and seek to explain, for example, how historians and those in English or Lettres françaises analyze texts, how scholars approach paintings, photography, and film, and how the study of music relates tempo and lyrics to wider societal trends. They utilize their respective research to elucidate means of effectively employing evidences and methods to achieve richer, deeper, and more nuanced results. As a whole, the collection provides an excellent primer for scholars of methodology.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".