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Record W4399829763 · doi:10.32920/26064034.v1

Gaze: A Digital Tool Designed for the LGBTQ+ Community to Confidently Navigate and Interact in Public Spaces

2024· preprint· en· W4399829763 on OpenAlexaffabout
T. de O. Pinto

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsGazeComputer scienceHuman–computer interactionWorld Wide WebInternet privacySociologyComputer vision

Abstract

fetched live from OpenAlex

<p>Members of the LGBTQ+ community are often on the lookout for spaces where they can freely express themselves without fear. These spaces include LGBTQ+ inclusive businesses, events, community centres, and organizations that are active advocates all year round towards a framework that supports equity, diversity, accessibility, and inclusion. Many people in the community operate from a scarcity mindset. It is a mindset that stems from the lack of spaces and the inaccessibility and difficulty of finding these spaces. This research paper and creative artefact, Gaze, focuses on promoting safe and inclusive spaces catered to Toronto's LGBTQ+ population, making them easier to identify and navigate. Toronto is home to Canada's largest LGBTQ+ population, and throughout the year, this community celebrates its creativity and diversity through various organizations and events. The creative artefact, Gaze, proposes to design a mobile application that combines services relative to digital directories, social media networks, and wayfinding technology. The goal is to enable the LGBTQ+ community to confidently navigate and interact in public spaces that prioritize their safety and embrace and empower those entrepreneurs who create them. With Gaze, there is potential to reflect the true diversity of Toronto's frequently overlooked LGBTQ+ population.</p>

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.536
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0030.001
Open science0.0020.006
Research integrity0.0000.002
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 head0.060
GPT teacher head0.330
Teacher spread0.270 · 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 designTheoretical or conceptual
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
Published2024
Admission routes2
Has abstractyes

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