CALICO at Center Stage: Our Emerging Rights and Responsibilities
Bibliographic record
Abstract
The year of languages (see www.actfl.org) in the United States is a good time to reflect on where CALICO is as a professional group of technology users, developers, and researchers. My thoughts on this issue come from my background and concerns stemming from my work in ESL in higher education. However, most CALICO members are likely to share at least some of my concerns. After all, higher education has a considerable impact on people throughout the profession— at least it should. In higher education, our mission, simply put, is to create and disseminate knowledge. Issues in ESL are sometimes seen as distant from those in foreign language, and there are some important differences, but when it comes to technology and language learning, a lot of common ground exists as well. Intellectually, we are all concerned with issues in applied linguistics—particularly issues of language learning, teaching, and assessment. Sociologically, we are positioned within departments of languages and linguistics, where we represent a minority. In fact, historically speaking, many CALICO members can recall their position as an eccentric minority in their language department. CALICO members were the strange professors who were writing programs for learners to study past tense verbs rather than papers on the underlying structure of gerunds or the influence of a Canadian author on the literature of the 1940s. Technology held a very marginal place in language departments.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.083 | 0.017 |
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".