Starting Sign Language Research from Scratch
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Starting Sign Language Research from Scratch Rachel I. Mayberry (bio) Perhaps the best way to illustrate the environment of sign language research when I began my graduate studies at McGill University is to note the physical labor involved. There were no internet or digital archives, and I spent a lot of time in the library searching for books and journals after first figuring out which floor and shelf the item's Dewey Decimal number pointed to. Journals could not be checked out, so notes had to be taken by hand, or exact change was required for xeroxing, if you could locate a machine. Statistical data analysis by computer was possible, but only by mainframe because desktops hadn't been invented yet. Videotaping of sign language was possible using huge reel-to-reel and then cassette recorders on large carts with heavy TVs and only somewhat less-bulky cameras and recording equipment. My first sign language experiment was for a course titled Language and Thought with Professor John Macnamara (1977). I compared concreteness ratings for English words with iconicity ratings for their ASL translations, which I gathered from sign-naïve undergraduate students. To create the experiment, I spliced reel-to-reel black-and-white videotape by hand with a razor blade and then used special tape to rearrange the segments for the experiment. I analyzed the data with paper, pencil, and a calculator. Manuscript preparation was tedious too, requiring typing everything out on paper, including tables, and making figures by hand with graph paper and either drawing them or using press-on symbols and letters. I have wondered how many [End Page 263] potential researchers might have gotten lost along this trail of work. But my father always said that I was stubborn, so I slogged through. More important than the labor, however, were the teachers and scholars who helped me along the way. Before attending McGill University, I had attended Washington University, where I came across the dictionary of signs with black-and-white photographs that Stokoe, Casterline, and Croneberg (1976/1965) had compiled using a coding system they had devised to represent sign structure. This was the only research I located in the library to help me explain sign language to the then-director of the Central Institute for the Deaf (CID), Richard Silverman (of Davis and Silverman 1978), who had asked me to teach him sign language once a week and made me promise not to tell a soul because sign language was forbidden. The institute included an oral school for deaf children, a speech and hearing clinic, graduate programs in deaf education, audiology, speech pathology, and research programs and faculty who primarily studied the sensory, perceptual, and motor mechanisms underlying speech and hearing. I have little memory of our meetings, except that he encouraged me to pursue sign language research by saying, "If we were to use sign language tomorrow, what should we expect from our students, from our teachers? You could study that." The idea was as intriguing as it was daunting. As a child, I hated questions about sign language and my parents' deafness from hearing adults, because I didn't know how to answer them. I worked as a dormitory assistant at CID while doing my master's degree. The experience reminded me daily of how imperative sign language was. Meals in the dining room were family style, and I was responsible for a table of eight students ranging in age from five to sixteen, all of whom were deaf and none of whom knew any sign language, and it was forbidden to sign to them. Some of the students could speak intelligibly and understood speech, but many others could not. There was a lot of gesturing and exaggerated oral gesticulation, and the students were good at helping one another understand what was being said among themselves and the staff. Saturday mornings, I was responsible for doing arts and crafts projects with the older students, some of whom spoke intelligibly and some who could not. Sunday afternoons, I was responsible for the "baby boys," the youngest [End Page 264] boys in the dorm, ages four to five. Another graduate student and I...
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it