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Record W2605391798

EXCAVATING OUR FUTURES- Exploring Participation in Experiential Scenarios

2016· other· en· W2605391798 on OpenAlexaboutno aff
Michael J. Stulberg

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2016
Typeother
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningPsychologyRigourInterpretative phenomenological analysisExperiential knowledgeFutures contractFeelingParticipant observationExperiential educationSocial psychologyQualitative researchPedagogySociologyEpistemologySocial science
DOInot available

Abstract

fetched live from OpenAlex

In recent years, 'Experiential Futures'—“the practice of making manifest, one or more fragments of an ostensible future world in any medium or combination of media...”—has been used to bring hard-to-imagine, future possibilities into immediate and observable settings (Candy, 3). Yet, central to what is supposed to make Experiential Futures 'tick' is an experience which, up until this point, has yet to be studied with rigour. This project uses Interpretive Phenomenological Analysis (IPA) methodology, to explore in detail the thoughts, feelings and sense-making activities engaged in by participants during a set of student-led experiential scenarios staged at OCAD University in Toronto, Canada. Using in-depth, semi-structured interviews and empathy mapping exercises, experiential scenario participants were asked to recall their experiences to the researcher. 
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\nThe outcomes of this study are threefold:
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\nI. a descriptive account of participant experiences during experiential scenario encounters. This account of participant perceptions, emotional responses and the formation of understandings, may help sensitize designers to their audiences.
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\nII. an analysis of the participant experience during experiential scenario encounters. The analysis may help illuminate some of the unique merits and potential weaknesses of using experiential scenarios to explore possible futures.
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\nIII. an identification of aspects of the participant experience underserved in today's experiential scenarios. Designers may use this to enhance the participant experience.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0040.002
Research integrity0.0010.001
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 head0.126
GPT teacher head0.369
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreOther

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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