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Record W4404315144 · doi:10.1145/3678884.3681904

Identifying the Values that Shape HCI and CSCW Research with Latin American Communities: A Collaborative Autoethnography

2024· article· en· W4404315144 on OpenAlexaff
Carla F. Griggio, Mayra Donaji Barrera Machuca, Marisol Wong-Villacrés, Laura S. Gaytán‐Lugo, Karla Badillo-Urquiola, Adriana Alvarado Garcia, Monica Perusquía-Hernández, Marianela Ciolfi Felice, Franceli L. Cibrian, Michaelanne Thomas, Carolina Fuentes, Pedro Reynolds-Cuéllar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsDalhousie University
Fundersnot available
KeywordsComputer-supported cooperative workAutoethnographyFormalityTransparency (behavior)SociologyValue (mathematics)Public relationsComputer sciencePolitical scienceEngineeringSocial science

Abstract

fetched live from OpenAlex

Over the past decade, community collaborations have come into focus within the HCI and CSCW fields. Largely the result of increased concern for social and contextual dimensions of practice, these partnerships facilitate a pathway for researchers and practitioners to foreground the nuances of technology as it takes place in the real world. How these collaborations are engaged, what values mediate them, and how practices might vary across geographies remain active research questions. In this paper, we contribute by zooming into the experience of four HCI and CSCW researchers engaging in community collaborations in Latin America (LATAM). Through a collaborative autoethnography (CAE), we identify three main value tensions impacting HCI practices and methods in research collaborations with LATAM communities: camaraderie vs. cautiousness, informality vs. formality and hopefulness vs. transparency. Building on our findings, we provide three recommendations for researchers interested in engaging in community-based research in similar contexts.

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 categoriesScholarly communication
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 score0.706
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
Bibliometrics0.0010.004
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.140
GPT teacher head0.400
Teacher spread0.260 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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