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Record W4200628236 · doi:10.17742/image.mm.12.2.5

Rendering Self and Microagressions Visible Through the Shadow Image

2021· article· en· W4200628236 on OpenAlex
Kim Snepvangers

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueImaginations Journal of Cross-Cultural Image Studies · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer graphics (images)Rendering (computer graphics)Shadow (psychology)Computer scienceComputer visionArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

This project, starting with Prompt 2 from the Massive Micro Sensemaking (MMS) led by Annette Markham and Anne Harris in May through June 2020, assisted me to move through the anxiety of COVID-19 lockdown. I set up four visual renderings—a series of photographs that, through a process of unfolding, make links to broader issues in my archival research in the context of settler colonial Sydney, Australia. Exploring lived experience through photography anticipates a creative a/r/tographic lens, focusing on rendering objects so that they take on a more-than-representational aspect, touching the materiality of objects as data. Adaptively layering the renderings moves beyond one dimensionality as a strict capturing of an observed phenomena. Here, an initial photograph has a latent, additional layer of shadow to build volume and re-cast semblances of the representational world through reflection.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0010.008
Open science0.0000.000
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 head0.028
GPT teacher head0.383
Teacher spread0.356 · 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