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Record W2773642456 · doi:10.1108/jpcc-02-2017-0005

Principals’ moral purpose in the context of LGBT inclusion

2017· article· en· W2773642456 on OpenAlexaboutno aff
Peter M. DeWitt

Bibliographic record

VenueJournal of Professional Capital and Community · 2017
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsTransgenderLesbianInclusion (mineral)HarassmentSafeguardingContext (archaeology)PedagogySociologyHomosexualityPopulationPsychologyQueerQualitative researchTransphobiaGender studiesSocial psychologySocial scienceMedicine

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to highlight the complicated nature of safeguarding lesbian, gay, bisexual and transgender (LGBT) students. First, this paper will address the issues that LGBT students face as a minoritized group in their school system, which inhibits their opportunities to reach their full potential when it comes to emotional and academic growth. Second, this paper will be used to discuss how leadership is vital in order for school communities to help address the issues that their LGBT population face. Third, the author will make the case that a lack of leadership self-efficacy can hinder the process. Design/methodology/approach Three different research studies were used to highlight the needs of LGBT students. However, there is additional research that is used as well to illustrate the need for leadership self-efficacy to support LGBT safeguards. When it comes to LGBT research the research of GLSEN (formerly known as the Gay, Lesbian, Straight Education Network) was used. GLSEN’s research consisted of 7,898 students between the ages of 13 and 21. Second, data from the Every Teachers Project by the Manitoba Teachers’ Society were used which involved 3,400 teachers around Canada. Although there are certain nuances between international examples which include those LGBT students living in the dominant culture as well as within indigenous populations, the author builds the case that the harassment and bullying has a common theme and can be addressed through common methods. Additionally, qualitative doctoral research was used, which consisted of 20 interviews of school leaders from three different school districts in New York State. Lastly, for the purpose of this paper the author will use the acronym LGBT to identify those in the lesbian, gay, bisexual and transgender community. There are many acronyms (e.g. LGBTQ, LGBTI, etc.) representing the community, and only when the research use those other acronyms, will those be used to highlight subgroup populations. Findings Findings indicate that, as a minoritized population, LGBT students are highly at risk for being verbally and physically harassed at school, and go unprotected by the adults who are in charge of keeping them safe. School leadership is instrumental in the safeguarding of LGBT students. Additionally, safeguarding is not nearly enough. It is important to understand that LGBT students should not just be safeguarded, but should also be surrounded by curriculum and images that will help them feel accepted into the greater school community, which takes an increased level of self-efficacy on the part of the leader. Originality/value The topic of engaging LGBT students in the school community is sparse at best. Additionally, this paper provides the case for safeguarding and engaging LGBT students, as well as all minoritized populations, but also discusses why it is the moral purpose of leaders to do so. However, the author believes that the addition of understanding leadership actions around safeguarding LGBT students through the lens of leadership self-efficacy and building collective efficacy is an important one, and will add to the originality of this paper.

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)0.025
metaresearch head score (Gemma)0.028
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 score0.040
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.028
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0400.060
Scholarly communication0.0200.008
Open science0.0020.019
Research integrity0.0040.011
Insufficient payload (model declined to judge)0.0060.001

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 head0.092
GPT teacher head0.423
Teacher spread0.331 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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