People-based marketing and the cultural economies of attribution metrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines People-Based Marketing (PBM) to theorize the cultural economies of attribution metrics. Through an analysis of marketing discourses, acquisition patterns, and marketing collaborations, it examines how platform capitalism is increasingly directed towards developing cross-device identity standards that consolidate performance metrics across digital markets. PBM extends the processes of platform capitalization across media properties, and the ways that claims of value and relevance are imbricated with the metricization of behavioral change in digital markets. The imperative of PBM to standardize techniques of identification and to make media increasingly measurable across markets has been a catalyst for new forms of data resolutions through strategic acquisitions and identity resolution consortiums. Moreover, emerging regulatory changes such as GDPR may in effect further reinforce trends towards the consolidation of data management and analytics platforms necessary to resolve identity across markets.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it