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Record W7071916856

In/visible families : exploring the experiences of lesbian, gay, bisexual, queer, trans-identified, and two-spirited parents in Northern Ontario school communities / by Natalie Rowlandson.

2017· dissertation· en· W7071916856 on OpenAlexaboutno aff

Bibliographic record

VenueKnowledge Commons (Lakehead University) · 2017
Typedissertation
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeHeterosexismPerceptionNarrative inquiryRepresentation (politics)Qualitative researchParticipant observationSemi-structured interviewSelf-disclosure
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study was to learn about the nature of interactions between Lesbian, Gay, Bisexual, Queer, Trans-identified, and Two-Spirited (LGBQTT) parents and their children?s teachers and school principals. A case study and narrative inquiry methodology was used to examine the research questions in this study. Semi-structured interviews were conducted to capture the experiences o f six parents in Northern Ontario. This method of study allowed for each participant to tell their own story about their thoughts, feelings, and perceptions about being an LGBQTT parent in a school community. Five common themes were identified during data
\nanalysis. These themes were: (1) coming out at school; (2) impact of disclosure on their children; (3) acceptance and validation; (4) lack of representation of non-normative families; and (5) high parental involvement in their children?s schools. The participants in this study emphasized the
\nimportance of full disclosure of family structure in school. They felt accepted and validated by school staff, but were acutely aware that homophobia and heterosexism nevertheless exists in school communities. The thesis concludes with recommendations for teachers, principals, and LGBQTT parents, as well as ideas for future research.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.105
GPT teacher head0.327
Teacher spread0.222 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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