What I learned about Linguistic Anthropology, Indigenous decolonization projects and Queer safe space from Deaf culture
Bibliographic record
Abstract
Conference paper presented at the Canadian Anthropology Society Annual meeting, Santiago de Cuba (2018). \n "My interest in Deaf Culture began a couple of years before the class. Douglas College, where I have a regular faculty appointment in the Department of Anthropology is also home to the largest and oldest Sign Language Interpreter training program in BC. As a result it is not uncommon to see people discussing in sign in the hallways, or to have sign language interpretation at college events, or even live interpretation as you teach (though transcription is more common). A happenstance reading of Andrew Solomon’s book "Far From the Tree" which addresses the gap between deaf children and their hearing parents spurred on my interest in Deaf Cultures and signed language peoples. I started to incorporate material on signed languages and Deaf Culture into my Intro Anthropology classes about three years ago. I positioned this material in our units on linguistic anthropology attempting holism with nods to biological, historical, and cultural influences on the composition of Deaf Cultures. Students were introduced to the Sapir-Whorf hypothesis —which, so far as I have read, is uncontested in Deaf Studies literature—and are asked to step outside the predominant audistic deafness-as-disability paradigm." -- Author.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.032 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".