Bibliographic record
Abstract
The Information Age has produced a society where data has become the principal commodity; where citizens are valued by the information they can provide to institutions. When applied to the democratic process, how will political campaigns utilize this technology to advance their campaigns? What is the impact of Big Data and predictive analytics on individual autonomy and how does this contribute to an increasingly fragmented society? The 2008 United States Presidential election instituted a new norm of political practice. The early stages of predictive analytics, provided by user generated data, enabled the campaign to isolate subsets of potential voters and persuade them into active participants. As the norm of quantitative campaigning became increasingly entrenched, the 2016 Trump campaign would demonstrate the current apex of its application. Utilizing sophisticated Big Data analytics, with support from Cambridge-Analytica and the Giles-Parscale agency, the Trump campaign created individual behavioral profiles of over 215 million voters. Who they would then strategically target to mobilize or de-mobilize the population in fault line States. The advent of the Internet enabled the development of mass scale data operations; when applied to quantitative marketing techniques, it allows for legacy institutions to strategically manipulate individuals to their preferred outcome. The predictive analytical techniques, that have been embedded throughout democratic societies are directly contributing to an increasingly fragmented society. As legacy institutions obtain more data they will increase their capacity to manipulate populations; changing the nature of political consciousness and contributing to an increasingly fragmented polis. Discipline: Political Sciences (Honours) Faculty Mentor: Dr. Jean-Christophe Boucher
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 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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".