Bibliographic record
Abstract
The rise of social media parallels a rise in distorted identity politics. The past decade has seen an increase in cases of ethnic fraud, polarizing populism, and corporate solidarity statements critiqued for capitalizing on social issues. While many critics blame these social phenomena on opportunistic individuals and businesses, this dissertation studies the role played by social media platforms: their business models, advertising datasets and ranking algorithms. I focus on Meta as a blueprint for subsequent social media platforms. I begin with my autobiography as a former QTBIPOC influencer, as a microcosm of the distorted identity politics amplified by social media infrastructures. I conduct a walkthrough of the Meta Detailed Targeting Tool (MDTT), paying close attention to how this tool fetishizes social difference: by stripping behaviors, interests and demographics down to fungible exchange value and market optimization. I argue that this process of fetishization rewards and conditions grammars of action that inherit the techniques, and problematic social dynamics, of Christian colonialism. This dissertation brings The Black Radical Tradition and anti-colonial critique to bear on social media scholarship, in order to investigate the following questions: 1) How does the MDTT shape behavior? 2) What are the motivations that compel identity politics? 3) Why have “morals” become so important to business? and 4) How can the framework of “piety” offer new theories and solutions for negative social media dynamics?
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".