Controlling Knowledge: Freedom of Information and Privacy Protection in a Networked World
Bibliographic record
Abstract
In an age where technology is rapidly changing how we communicate in both our personal and professional lives and privacy issues frequently make headline news, Lorna Stefanick's Controlling Knowledge: Freedom of Information and Privacy Protection in a Networked World is extremely timely.Writing for a general audience and including many real-life examples, Stefanick unpacks the complexities of freedom of information and privacy protection (FOIP) and explains how these concepts relate to governance-sparking my interest as a continuing education administrator because instructors have been increasingly inquiring how to incorporate activities using publicly accessible external sites such as blogs, wikis, or Facebook into their teaching.Many instructors, though enthusiastic about the potential benefits of these tools, may be unaware of privacy issues associated with their use in education or uncertain how they will affect their teaching strategies in practical terms.As Stefanick asserts, many of us are familiar with the basics of FOIP but are complacent about protecting it until either our privacy or ability to access information is threatened or removed.Indeed, the proliferation of new technologies makes it difficult for the average person to become familiar with the privacy policies and potential issues pertaining to each new technology.Educators will therefore benefit from Stefanick's discussion about how technology has changed the ways in which information is captured, stored, and disseminated.As well, this book may benefit university administrators who play a role in defining FOIP principles, values, and policies at their institutions.The first half of the book lays the conceptual groundwork for privacy and access to information as they relate to legislation, as well as introduces relevant cultural influences and historical events from Canada and around the globe.Stefanick begins with a discussion of the challenges in defining privacy, the reasons that privacy protection is valued by society, and the importance of shifting norms of privacy.Stefanick then delves into examples of how personal autonomy may be threatened by the rapid and efficient data flow recent technologies have enabled.This discussion leads to the juxtaposed topic of freedom of information (FOI) or, more specifically, the idea of transparency-a necessary condition for accountability-and it becomes apparent that "the point at which transparency becomes an infringement on the ability of individuals or a group of individuals to pursue their self-interest without undo interference from Reviews /
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".