Identifying critical factors for developing effective rural community technology centers
Bibliographic record
Abstract
The purpose of this research is to inform both existing and developing community technology initiatives as to the critical factors for building effective rural community technology centers. Rural community technology centers which had been operating for at least two years were identified and contacted by telephone. Either a paid or volunteer staff person was interviewed using a semi-structured protocol of open-ended questions. Responses were taped, transcribed and coded using standard tools and procedures for qualitative investigation. Codes were grouped in 12 thematic groups. Relative occurrences of codes within each group were analyzed. Participants were asked what criteria were used to measure effectiveness of their centers. Participants also made recommendations about alternative evaluation metrics that could be evidence of the impact of their centers on participants. The findings suggest eleven areas that require attention when developing rural community technology centers or networks. Results also support Maughan's model of a robust communication system and Kling's Social Informatics theory.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".