Bibliographic record
Abstract
Introduction Citation counts are well-established measures of researchers’ scientific impact. One would assume that external factors, such as someone’s name, over which an individual has little control over, does not influence such indicators. Yet, reference lists and— to a lesser extent—search results from online databases, are often presented in alphabetical order sorted by first author surname. A large number of scientific journals use parenthetical referencing styles (a.k.a. Harvard referencing style) in which partial parenthetical citations (such as author+date or author+title) are embedded in the text, accompanied by an alphabetized list of complete citations at the end. These lists may be consulted to locate a specific item (known-item search) but are also used in a scanning mode, usually from top (A) to bottom (Z), to identify papers that would potentially provide answers to a question or reinforce an argument. In marketing and advertising research it is well recognized that product positioning influences choice and selection and that usually “first is best”, i.e., that items presented first usually have a better chance of being selected (Carney & Banaji, 2012). Such a phenomenon has also been observed by Haque and Ginsparg (2009, p. 2215) who measured a significant correlation between article position in the arXiv repository and citation impact, due the “visibility” effect that “can drive early readership, with consequent early citation potentially initiating a feedback loop to more readership and citation.” Order of presentation (or scanning order) is also central to Cooper’s utility theory (1971) since items consulted earlier will find a better chance of being useful to a searcher. Taking these elements into account, authors with a surname whose initial letter arrives early in the alphabet get more visibility, a situation that is further compounded by the fact that in multi-authored papers, authorship order is sometimes determined by alphabetical rank. This practice is even fairly common in some fields such as economics and finance, mathematics, high-energy physics, marketing, political science, international relations and law (Frandsen & Nicolaisen, 2010, p. 615; Levitt & Thelwall, 2012, p. 725; Waltman, 2012, p. 701). In the field of economics where authorship order is almost always determined alphabetically, research has shown that economists with early surnames (i.e., with initial letters that occur early in the alphabet) publish more articles (van Praag & van Praag, 2008), are more likely to get employment at high standard research departments (Efthyvoulou, 2008) and receive more tenure at top economic departments (Einav & Yariv, 2006), since “the order of authorship, rather than contributorship, is commonly used to assess the prestige that an author incurs from a published research study” (Chambers, Boath, & Chambers, 2001, p. 1461).
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 | 0.017 | 0.270 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.028 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.208 | 0.115 |
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".