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Record W4210977609 · doi:10.32920/ryerson.14658009

Building bridges: exploring gender through photographic practice

2021· preprint· en· W4210977609 on OpenAlexaboutno aff
Jennifer K. O’Leary

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicPhotography and Visual Culture
Canadian institutionsnot available
Fundersnot available
KeywordsIdentity (music)Meaning (existential)FeelingAestheticsVisual artsSet (abstract data type)Human sexualityIdeologyPerspective (graphical)SociologyPsychologyArtSocial psychologyGender studies

Abstract

fetched live from OpenAlex

This project is a visual expression of my observations about gender within western culture. My photographic practice is conducted within and mediated by significant beliefs about gender and, in turn, provides ideological support for how I relate to society. Acknowledging that I photograph from a female perspective I photographed both male and female subjects of different genders and races using a 35mm camera with a wideangle lens. I captured images that helped me reflect on my own practice as a photographer. My images can be viewed as individual photographs or as a set. Factors, such as my cultural background, social status, religious beliefs, and level of comfort with my own sexuality, influenced my photographic practice and so will inevitably affect how viewers respond to my images. How I feel about identity construction permeates through out my image making process. As a photographer in the Ryerson University and York University joint program of Communication and Culture exploring different theoretical frameworks undoubtedly affected my studio practice as I gained more knowledge and became more self-reflective. I accept as photographer that my images will not have a fixed meaning but I do intend them to evoke feelings. Since I discovered Henri Cartier-Bresson's work as a young teenager I have always had a profound respect for his abilities and his methodology. Although I would never begin to align my work with a master photographer with regards to quality I have always aspired to his greatness. Robert Frank and Eugene Richards also have inspired me during this Masters project. My more recent appreciation of their work reinforces my belief that there will always be a place for striking 'documentary' style photographs taken on film and printed on fiber based paper by the hand of one whom feels the call ofthe traditional darkroom.

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)0.010
metaresearch head score (Gemma)0.013
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 score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0100.016
Scholarly communication0.0080.008
Open science0.0020.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.254
GPT teacher head0.341
Teacher spread0.087 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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