Bibliographic record
Abstract
As smart technologies become more integrated with daily life, vital digital literacy skills are necessary for citizens to engage with and benefit from their cities, local government, and economy. Libraries play an important role in mitigating the growing wealth gap in our communities, especially as it relates to opportunities provided by emerging technologies. With the call for smart city proposals in Toronto, Ontario, what role will the city's LAMs have in collaborating with these future developments? The Toronto Public Library (TPL), a trusted public institution, has a stake in implementing various frameworks and collaborating with government agencies in addressing public concerns around technologies that collect personal information for various purposes and ensuring that vulnerable populations are not left behind. Following an examination of the role libraries play in mitigating consequences of the digital divide, this chapter will discuss the various ways in which TPL and similar community libraries have been involved with digital literacy and inclusion. It will also explore how TPL has been identified by government agencies as a vehicle for civic engagement and oversight in the former Sidewalk Toronto smart city plan.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.203 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".