Identifying the Values that Shape HCI and CSCW Research with Latin American Communities: A Collaborative Autoethnography
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".