Bibliographic record
Abstract
BACKGROUND he popularity of gift cards in recent years has soared.According to Statistics Canada, currently 82% of large retailers offer gift cards, whereas only 53% of large retailers did in 2003. 1 Similarly, the American gift card market grew to $45 billion U.S. in 2003 from $1 billion in 1995, and in 2007 projections suggest that the gift card market will reach $70 billion U.S. 2 Gift cards also comprise a significant portion of retailer revenues.For example, Starbucks has reported that 11% of its North American revenues consist of gift card purchases.3 Consumers like gift cards because they are convenient and are often the perfect gift for the friend or relative who has everything.Retailers like them because they can attract a new customer base, and consumers usually spend more than the value of their gift card once in the store.4 They are also difficult to counterfeit, can often be reloaded, and retailers can analyze spending patterns and behaviours through their use.5 Another significant advantage for retailers is that a certain proportion of gift cards are never redeemed by the consumer, and even if they are eventually cashed in, the retailer still has the advantage of investing that outstanding amount until the card is redeemed.6 Sometimes these 1 Bahta et al, "Gift Cards: The Gift of Choice", (December 2006), online: Statistics Canada [Bahta]. 2 Maryanna Lewyckyj, "Legislation will rein in retailers who cash in on early expiry dates", The Toronto Sun
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.027 | 0.014 |
| Insufficient payload (model declined to judge) | 0.049 | 0.050 |
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".