Bibliographic record
Abstract
On May 11th 2004, Industry Canada’s Minister Lucienne Robillard announced the creation of the Task Force on Spam whose mandate was to oversee and coordinate the implementation of a national plan to control spam (unsolicited emails). Spam, whose origins go back as far as 1975 (RFC 706), constitutes for now one of the most urgent threat to the Internet as its infrastructure is somehow limited. In May 2004, junk email accounted for nearly 70% of emails worldwide (BBC, 2004), reflexively threatening the viability of the Internet and the e-commerce. What explains the government’s involvement in the fight against spam? Weren’t experts able to manage this problem, which seems technological a priori? We need to acknowledge that spam is too social, technical, economic and political at the same time for a traditional decision to be capable of managing it. The experts’ incapacity is a manifestation of great complexity, but it is also especially one of growing uncertainty. In order to describe the uncertainty surrounding spam, we propose to view the phenomenon as a symptom of a “modernity” that promotes the proliferation of hybrid risks that can no longer be purified. We shall see that the uncertainty concerning spam has made it possible for new management procedures to emerge. It is at least the ambition of the Task Force on Spam to do so. First of all, this dissertation will present the social, political and economical reasons that led to the creation of the task force. Why did Industry Canada inherit of dealing with spam? What are the actors around the government, and what interests are at stake? Answers to these questions will bring light to an impressive controversy between two competing projects. Our ethnographic observations made to the TFS enabled us as well to explore the strategies put in place by societies when facing phenomenon that are global and uncertain in late modernity. Our work postulate that spam is a relevant illustration of issues linked to the diffusion of information and communication technologies. Using the controversy of spam, we intend to show that uncertainty can possibly open the door to new models of democracy and that the rivalry between the agenda of two social groups might also be the manifestation of competing risk-management models. We were able to identify in those models the influence of two different types of democracy: delegative and deliberative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.019 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".