A case of Intention Deficit Disorder? ICT policy, disadvantaged schools, and leaders
Bibliographic record
Abstract
Since the mid-1990s, government policies in the USA, Canada, England, and Australia have promoted the need to produce an ICT skilled workforce in order to ensure national competitiveness in globalised economic conditions. In this article, we examine the ways in which these policy intentions in 1 state in Australia were translated into a techno-determinist and technocentric plan which focused primarily on getting wired up and connected. We summarise the findings from 2 projects: an investigation of a state-wide principals' professional development programme and an action research study investigating literacy, educational disadvantage, and information technologies. We found significant differences in the distribution of the physical and human capabilities between schools which made the task of engaging with ICT harder for some than others. Nevertheless, we suggest that some school leaders did develop innovative practice. We suggest that policy deficits made it difficult for school leaders to grapple with the dimensions of and debates about the kinds of educational changes that schools and school systems should be making.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".