Perceptions on the Ground: Principals’ Perception of Government Interventions in High-Speed Educational Networking
Bibliographic record
Abstract
The Alberta SuperNet was built to bring broadband connectivity to every school, hospital, library and provincial government office in Alberta (a large province in Canada with an area of 255,285 square miles). The supposed benefits of high-speed access have led to calls for strategic public investment on both the supply and demand sides. The provincial government, through Alberta Education, initiated a number of interventions to help make broadband technology more useful and accessible to Alberta schools and to promote use of the new technology. To investigate the perceived efficacy and awareness of these initiatives, a survey of school officials was conducted in the spring of 2005. The survey was designed to assess the interest, awareness and planned use of high-speed networking initiatives by school officials. The results of the survey show that principals place relatively high levels of importance upon these initiatives but their level of awareness of, and especially their utilization of the initiatives was much lower. There were small but significant differences among principals from large versus small schools and between principals from rural versus urban school. The paper concludes with recommendations for policy makers and administrators challenged with creating effective interventions using broadband networking.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".