Migrant and Seasonal Farmworkers in Digital Inclusion Planning, North Carolina, 2023-2024
Bibliographic record
Abstract
Digital exclusion is a challenge in rural North Carolina (NC), USA, where agriculture is the leading industry. Agricultural workers such as migrant and seasonal farmworkers (hereafter “farmworkers”) are disproportionately impacted by digital exclusion. As part of an effort to address digital exclusion, funders and state agencies in NC have promoted the development of county and regional plans for digital inclusion. From July 2023 to July 2024, we identified a total of 30 digital inclusion plans that covered 50 of NC’s 100 counties. To assess inclusion of farmworkers, we used a quantitative content analysis approach with two independent coders. No digital inclusion plans included farmworkers in their needs assessments or goals. Just 7% of digital inclusion plans included farmworker organizations in their planning and development, 13% of plans noted agriculture as a topic of interest in their needs assessments, and 40% noted agricultural technologies as a topic of interest. None included short or long-term goals related to agriculture. The general invisibility of farmworkers in plans contrasts with greater attention given to agriculture-related technologies. Additional attention must be given to ensure farmworkers are involved in future digital inclusion efforts.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".