Information technology in schools: Should the product be marked hazardous?
Bibliographic record
Abstract
One of the things that make some tools hazardous is that when you plug themin you have considerable power in your hands. False moves and you maylose a body party. ICT, I argue in this paper, is such a tool and yes it shouldbe marked hazardous. How powerful? Why hazardous?The power and the hazard go together. By means of ICT we can do morequickly and comprehensively what we could only do more slowly before. Andif aspects of what we were doing before were problematic, they will be evenmore so thanks to the amplification provided by ICT. The amplifying powerof ICT is one of the central themes of this paper. If instruction is routine andboring, computers can make it much more so. If products for schools weredriven by commercial considerations before, they can be much more so withICT - the investments are much higher. If schools had ways of monitoringteachers and children before, computers enhance that many times. Iftechnologies were thought to drive what happens in schools before, ICTmagnifies that potential many times.In short, computers allow us to do what we did before only more so - so if wedid not do well before IT, we may well do worse with it. Or we may dobetter. We need to keep these questions open. The biggest danger is that in therush to conform to visions of ICT we may stop asking them. We need adialogue. The desire for dialogue has, however, frustrated many ICTadvocates who see it as a form of resistance to change (Ameral, 1983; Papert,2001; Yelland, 2002). From a philosophical point of view, we need a dialoguethat considers the techniques of practices and the goods that such practicesseek, as Macintyre (1981) and Strike (nd) point out. Practices involve skillsand techniques - which do not once and for all define the practice and whichover time change the practice as the goals of practice - in this case teaching -change over time.In the first part of this paper I look at how ICT has come to play such adominant role in schooling. Over the last 20 years we see a continuous pressto adopt this technology. What we do not see is a debate about the plussesand minuses of computers in schools based on experience. This is a cause forconcern. In the second part of the paper I look at what happened in schoolsover this time and the beginnings of concerns about ICT. In the third part ofthe paper I look at the response teachers might make to ICT as it evolves.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".