From ‘Disengaged’ to Digital: Latino Boys as Emergent Technology Experts
Bibliographic record
Abstract
Anthropological research suggests that young Latino men face a complex set of cultural norms that can make it difficult to identify as experts in technology. In education research Latino boys have historically been framed in relation to deficit-perspectives. This dissertation provides an account of Latino boys as self-efficacious learners. I ask, a) how did a group of Latino boys integrate their cultural values and extant technology-related learning practices into self-directed digital literacy learning?; b) What pertinent social processes shaped their emerging sense of themselves as members of a group of digital literacy learners?; and, c) How did these emergent technology experts formulate narratives about their educational trajectories and imagined futures based on their technical and social practice skills development?\nOver eighteen months, eight Mexican-origin middle school boys met in a community center technology room. Utilizing a critical ethnographic approach, I observed the boys self-directed digital literacy practices, including coding, 3-D drafting, and graphic design. I collected 55 hours of audio recordings, semi-structured interviews, and participant products, as well as producing field notes and analytic memos to reflexively evaluate emergent concepts.\nThe ethnographic accounts suggest a mode of social identity formation wherein the boys applied their cultural values and learning practices to self-directed endeavors. A pattern emerged in which the boys acknowledged the specialized skills of each other, and in turn, had their own forms of digital literacy expertise recognized. I contend the participants came to understand themselves and fellow group members as emergent digital literacy experts, developed communicative repertoires aligned with technologically proficient social identities.\nThis research contributes to scholarship in cultural anthropology, education/learning sciences, and Latinx studies. This research pushes the boundaries of critical ethnography by positioning the researcher as an activist-scholar. It focuses on how learners from non-dominant groups can develop identity trajectories in relation to their informal STE(A)M learning practices. And, it interrogates how a Xicanx researcher can participate with Latinx youth as a coordinator of dialogue, a navigator of educational spaces, and a colonizer valuing neoliberal ends/trajectories.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".