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Record W4321022352 · doi:10.22329/jtl.v16i3.6886

Enduring Effects: Name Mispronunciation and/or Change in Early School Experiences

2022· article· en· W4321022352 on OpenAlexaffvenueabout
Tina Bonnett, Bonika Sok

Bibliographic record

VenueJournal of Teaching and Learning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicNames, Identity, and Discrimination Research
Canadian institutionsFanshawe College
Fundersnot available
KeywordsHonourIdentity (music)PhenomenonPsychologyPedagogySociologySocial psychologyLinguisticsAestheticsHistoryEpistemologyArt

Abstract

fetched live from OpenAlex

A person’s name(s) is typically tied to their family, culture, and sense of identity. Consequently, when a child’s name is inaccurately pronounced, altered, or avoided, a host of adverse consequences may transpire. Although seemingly innocuous, this necessitates attention, as name mispronunciation and change perpetuate microaggressions ubiquitous for marginalized populations, often in school contexts. In reflection of this, an Intrinsic Case Study, underpinned by a Social Constructivist Philosophical paradigm, was conducted to assemble the experiences of three adults in Ontario, Canada, who had their names mispronounced or changed in early educational experiences. The findings of this research signify that name mispronunciation and modification are pervasive and that teachers are often central contributors to this phenomenon. Moreover, findings denote that discord between one’s identity and cultural self is affiliated with name-orientated microaggressions. Participants of this study beseech teachers to denounce insensitive practices by pledging to accurately pronounce and honour each child’s name and in so doing engender more favourable longitudinal outcomes.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.366
Teacher spread0.328 · 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 teacher head, not a consensus.

Study designObservational
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
Published2022
Admission routes3
Has abstractyes

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