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Record W3176488010 · doi:10.22215/etd/2021-14369

Stories Teachers Tell: A Narrative Exploration of American Sign Language Teachers’ Professional Lives

2021· dissertation· en· W3176488010 on OpenAlexaffabout
Christina Dore

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsCarleton University
Fundersnot available
KeywordsAmerican Sign LanguageNarrativePedagogyContext (archaeology)Deaf educationSign languageNarrative inquiryProfessional developmentStorytellingPsychologySociologyLinguisticsHistory

Abstract

fetched live from OpenAlex

American Sign Language (ASL) is one of the most popular languages for foreign language study among hearing adult learners.ASL teaching has deep connections to the long-standing oppression of deaf people and sign languages, and so ASL teachers might be linguistic and cultural guides as well as deaf advocates, allies, and spokespeople.Yet, little research has addressed who ASL teachers are and how they have come to and navigated the profession.In Canada, there is no clear educational pathway to learning to teach ASL.Although formal and informal teacher learning opportunities exist, they are not widely adopted or enforced.In response, this narrative dissertation explored the professional life histories, or pathways, of seven ASL teachers in Canada through multi-part interviews.I was informed by theories of narrative as a social practice and teacher learning as embodied, and prior literature about the sociohistorical context of ASL and ASL teaching in North America.To further contextualize teachers' stories, I also conducted interviews with representatives from deaf cultural organizations and ASL program administrators and incorporated publicly available data about ASL in Canada.The findings of this study were an extensive collection of stories drawn from the seven teachers' accounts, organized into three chronological clusters: early ASL and teaching experiences (pathways to teaching), ongoing teaching experiences (pathways through teaching), and reflections on experience (pathways forward).Stories about teachers' early experiences underscored the diversity of people that comprised this workforce-native iii and non-native signers, deaf, hearing, and hard-of-hearing, formally and informally trained, and so on.Their accounts of ongoing practice illustrated how they variously strove to be teachers and the different successes and challenges they met along the way in meeting their goals.Teachers' closing reflections demonstrated that their teaching work was tightly intertwined with other goals, including the broader social justice aim of improving the status of sign languages and deaf people in Canada.This study aimed to make a space for ASL teachers in academic conversations where they are rarely featured.I hoped that ASL teachers, especially the study's participants, found meaningfulness in reflecting and sharing professional experiences documented in this dissertation.Dr. Janna Fox, my supervisor, thank you for everything.Without you this "beast" would never (ever) have finished-or started, for that matter!Thank you for your infinite patience and wisdom over these many years, and for being the loudest cheerleader in my sprint to the finish line.Dr. Natasha Artemeva, thank you for your calm and nurturing guidance always, from my first campus visit in 2011 to the end of this Ph.D. nearly ten years later.Thank you for all of the great theorists you brought to life.Dr. Samah Sabra, a.k.a."who I want to be when I grow up", thank you for showing

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0210.015
Scholarly communication0.0100.011
Open science0.0020.009
Research integrity0.0030.009
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.053
GPT teacher head0.407
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes2
Has abstractyes

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