Bibliographic record
Abstract
Abstract McDonald’s, the seemingly invincible fast-food giant and stalwart of American business, has been an ironclad money-making machine for decades now. Sure, they’ve had their issues with bad press concerning the negative health effects of their food, but otherwise the company is as sure to post profits quarter after quarter as anyone. At least that’s been true until recently, as it appears that McDonald’s has hit a bit of a snag. Starting out with just one burger stall in 1948, the fast-food chain’s emphasis on quick service and a standardized menu has helped it to grow to more than 35,000 outlets across the world. It has been profitable: after a wobbly period in the early 2000s, the firm’s share price went from $12 in 2003 to more than $100 at the end of 2011. But now McDonald’s has lost its sizzle. Global sales have been declining at least since last July. When the company announces its annual results on 23 January, analysts think it will reveal its first full-year fall in like-for-like revenues since 2002. What’s gone wrong?
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.011 |
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".