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Record W4403558941 · doi:10.1145/3686169.3686204

The Inbetweeny Collective: Reflexive Dialogues on the Liminality of Researchers' Lived Experiences

2024· article· en· W4403558941 on OpenAlexaff
Denise Quesnel, Tatiana Losev, Ekaterina R. Stepanova, Sheelagh Carpendale, Bernhard E. Riecke

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsLiminalityReflexivityLived experienceSociologyComputer scienceMedia studiesEpistemologyPsychologyPsychoanalysisAnthropologyPhilosophy

Abstract

fetched live from OpenAlex

In this collaborative autoethnography, we critically explore our lived experiences within a wider context of HCI research and practice. We reflect on the epistemological ways of knowing through ‘insider’ lived experience of, and ‘outsider’ knowledge of our research topic(s) via the concept of “liminal space” as a process ontology. To embrace liminality entails inhabiting the space ‘in-between’ these ways of knowing, suspended on a threshold of uncertainty and transformative growth. All authors identify as ‘inbetweenies’, because we are neither just ‘insiders’ nor ‘outsiders’, and we collect our respective stories to share. Drawing from these stories and our dialogues, we discuss how ways of knowing have historically been dichotomously categorized with their associated subjective or objective characterizations, resulting in power hierarchies and tensions. We propose that for an ‘inbetweeny’ researcher, thoughtful approaches to navigating this liminal space could potentially bridge persistent tensions in HCI research and practices toward personal and systemic transformation. Five areas of reflection are discussed, with proposed learnings that can be applied towards sustained practices for individuals and collectives at any stage of their journey and development.

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)0.031
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.716
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.006
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.672
GPT teacher head0.615
Teacher spread0.057 · 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; both teacher heads agree on what is shown here.

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
Published2024
Admission routes1
Has abstractyes

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