Bibliographic record
Abstract
Because of their uncertain status as consumers, the globesity controversy focused public attention on the degree to which children are exposed to food advertising targeting them. A penetrating spotlight was cast on the magnitude of marketing resources devoted to selling energy-dense foods on television. A quick glance at the adspends reminds us that food products are prominent in global advertising spends. Harris et al. (2002) documented more than 20 per cent increases in total US food advertising spending from 1995 to 1999. The magazine Advertising Age attempted to estimate advertisement spending in terms of measured media purchase in 2005. It placed the spending of global food advertising at US$8129 million, soft drink advertising at US$3971 million, restaurant advertising at US$3349 million and candy advertising at US$1109 million (Endicott 2005). Overall, these food-related categories accounted for 16.8 per cent of the total amount spent on advertising in 2005 (Advertising Age 2006: 7). Of this approximately 65–70 per cent of all food spending was devoted to television (Warren et al. 2008). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".