Critical Importance of Emphasising Working-Class Parents in Digital Inclusion: A US Latino/a Case Study
Bibliographic record
Abstract
In this article, we draw on extensive qualitative data to analyse the specific case of a digital inclusion program launched by the non-profit organisation River City Youth Foundation, located in Central Texas. The case is particularly interesting because the organisation, which is primarily a youth centre, realised they needed to start including parents in their programs in order to achieve their first and foremost institutional goal: to increase the number of low-income youth in US colleges. For this study, we use Pierre Bourdieu’s theories of capital to analyse how the organisation integrates education in their digital inclusion program—called ¡TechComunidad! — and thus how they instil techno-dispositions and cultural capital about how US education works in parents of children in kindergarten to 12th grade (K-12). This case is also relevant because it is related to a specific community of low-income Latino immigrants, mostly of Mexican descent, who live in a neighbourhood, where most of the residents are Hispanic. The ¡TechComunidad! program may take between six and eight weeks, and at the end of the training, grants participants a Chromebook – a laptop with a Google OS that only works with internet connectivity. Our results suggest that the organisation managed to instil techno-dispositions and knowledge of education, but parents may still face other sorts of divides, once they bring their Chromebook home.
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 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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".