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Record W2900611471

Dis/ordered Mapping/s of the Canadian Museum for Human Rights

2016· article· en· W2900611471 on OpenAlexaboutno aff
Janice Rieger

Bibliographic record

VenueQUT ePrints (Queensland University of Technology) · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicCrafts, Textile, and Design
Canadian institutionsnot available
Fundersnot available
KeywordsEmbodied cognitionMateriality (auditing)NarrativeUndoingAestheticsFraming (construction)SociologyArticulation (sociology)InterdependenceEpistemologyArtPsychologyLinguisticsHistoryPhilosophyPolitical scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

The doing of dis/ordered mapping/s is not about fixing lines and encounters in order to produce a map or model; on the contrary it is about exploring differing embodiments and material relations among people, things and disability. New narratives flow out through the actions of the fingers and body by doing collaborative mappings through embodiment. Narratives are of/from particular places and are mapped out through particular embodied experiences. It is through the doing of collaborative fibre mappings to analyse embodiment that we realized how much the materiality of things within two national museums in Canada pressed and pushed upon us. Through our struggle of giving in to and following materiality, we realized that doing of mapping/s are continual performances of making, unmaking, doing and undoing through people’s embodied abilities. The complex braiding of this research suggests a need for a more holistic exploration of inclusion in museum spaces through an embodied articulation because users are moving and performing unconsciously with their bodies at all times. Through a doing of dis/ordered mappings alternative ways of approaching, framing, doing and narrating interior spaces open up new knowledge processes, engagements, methodologies and methods through an embodied criticality that allows for a kind of stumbling and playing across/with new encounters.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.191
Teacher spread0.169 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2016
Admission routes1
Has abstractyes

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