Paying Attention: Big Data and Social Advertising as Barriers to Ecological Change
Bibliographic record
Abstract
Big data and online media conglomerates have significant power over the behavior of individuals. Online platforms have become the largest canvas for advertising, and the most profitable commodity is users’ attention. Large tech companies, such as Facebook and Alphabet, use historically effective psychological advertisement tactics in tandem with enormous amounts of user data to effectively and efficiently meet the needs of their customers, who are not the end-users, but the corporations competing for advertising space on users’ screens. This commodification of attention is a serious threat to socio-ecological sustainability. In this paper, I argue that big data and social advertising platforms, such as Facebook, use commodified attention to take advantage of psycho-social neuroticisms and commodity fetishism in modern individuals to perpetuate conspicuous consumption. They also contribute to highly fragmented information ecologies that intentionally obscure scientific facts regarding ecological emergencies. The commitment to stakeholders and growth economics makes social advertising conglomerates a significant barrier to a socio-ecological future. I provide a series of solutions to this problem at the institutional, research, policy, and individual levels and areas for future sustainability research.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".