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Record W3203865404 · doi:10.1108/mhsi-08-2021-0055

Research watch: routes to marginalised students’ increased inclusion and empowerment

2021· article· en· W3203865404 on OpenAlexaboutno aff
Sue Holttum

Bibliographic record

VenueMental Health and Social Inclusion · 2021
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentInclusion (mineral)IndigenousOriginalityCritical consciousnessConsciousnessMental healthPedagogyFocus groupSociologyGender studiesPsychologyPublic relationsPolitical scienceSocial scienceQualitative researchPsychotherapistLaw

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper was to report on recent research about how students belonging to marginalised groups can be empowered. Design/methodology/approach The author searched for articles that covered the topic of empowerment, published in the past two years. The author selected two papers that each focus on a different group and illustrate processes of empowerment applicable in their contexts. Findings The first paper deals sensitively with the topic of in-fighting amongst Indigenous students at Canadian universities and how Canada’s colonisation history contributes to this. It also illustrates how Indigenous students are working together to improve universities’ recognition of their needs and rights. The second paper describes a consciousness-raising programme for Black girls in secondary schools in Pennsylvania, USA. Black girls attending the programme valued it and felt more connected with other Black girls. There was some dropout from the programme, but those who remained appeared to benefit. Originality/value These two papers represent important illustrations of some complex challenges facing marginalised groups and how their empowerment and inclusion can increase, with implications for their mental and physical well-being.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0320.000
Scholarly communication0.0000.000
Open science0.0000.022
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 head0.111
GPT teacher head0.522
Teacher spread0.412 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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