Bibliographic record
Abstract
Surveillance capitalism is defined by Dr. Zuboff, Harvard professor of social psychology, as the “commodification of reality and its transformation into behavioural data for analysis and sales” which is invading our modern world. Surveillance capitalism can occur through the use of social media and search engines; these platforms provide third party companies with large amounts of data that allows them to predict how we, the consumer, will behave. As a result of using their platforms, these companies learn how to modify our behaviour through the use of this complex behavioural psychology, and we put ourselves at risk of manipulation without even knowing it. Surveillance capitalism is a new force that is emerging in medicine and consequently becoming a new public health concern. This commentary will discuss the way surveillance capitalism affects public health through the use of Google, social media, and apps. Surveillance capitalism is on the rise in our society, and it is hard to stop it from invading our personal and private lives. Steps towards resolving this emerging public health problem involve better systems in place to protect consumer data, encouraging consumers to think critically about the information they see online, and funding more research to understand the ethical implications of these platforms in use.
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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".