Proceedings of the 2022 International Conference on International Studies in Social Sciences and Humanities (CISOC 2022)
Bibliographic record
Abstract
This book brings together a selection of papers presented at The 2022 International Conference on International Studies in Social Sciences and Humanities (CISOC 2022), an international event organized at the Université du Québec à Trois-Rivières (Canada), with the support of the Universidad del Rosario (Colombia) and the University of Nottingham (UK). It took place at Trois-Rivières, Canadá, during 28–29 July 2022.CISOC 2022 was conceived as a space for connection, debate and networking among delegates from the Global North and the Global South. As such, the event stimulated conversations, dialogues and discussions on a range of topics in the Social Sciences and Humanities—including neighbouring disciplines such as the Arts. In the 2022 edition, 46 authors from 10 countries (Australia, Canada, China, Colombia, Ecuador, France, Nepal, Pakistan, Peru, Spain) sent their proposals. Each proposal was selected after thorough editorial and rigorous peer-review processes. The Program Committee of CISOC 2022 was composed of a multidisciplinary group of 36 experts from 14 countries who evaluated each paper in a ‘double-blind review’ process.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.105 | 0.043 |
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".