Can U B A Can Writer? Child Abuse and Neglect: Identikit of the Model Author
Bibliographic record
Abstract
Background: Child Abuse and Neglect (CAN) represents a relatively young theme in the world of research. However, more than 50 000 manuscripts dealing with it have been published since its birth, classically considered the Kempe’s paper, “The battered child syndrome”, published in 1962. Such an amount of papers has allowed the blooming of knowledge, and more and more people got involved in understanding the phenomenon. In the last few years, a decrease is observed in the interest this issue is able to evoke in the scientific community (measurable in the progressively lower number of manuscripts published). Identification of model contributors and estimation of prevalence of group and corporate authorship could represent a good tool to understand how to enhance the number of publications, considered as the currency of academic research. Objective: Aim of the present study is to draw an identikit of the model researcher involved in investigating the issue of child abuse and neglect. Method: A retrospective bibliometric analysis was carried out, utilizing the search engine of NIH, PubMed. The last 10-year literature (1995-2004) was explored and the quoted phrase “Child Abuse” was searched in all available manuscripts. Starting from retrieved papers, information on authors was collected. Particular attention was paid to affiliation, country of origin, preferences shown on country of publication, tendency to publish in groups or corporate authorships. Starting from the Web of Science, 50 most prolific investigators were studied for the number of citation they showed. A descriptive analysis was performed. Results: The bibliographic research retrieved 7369 articles, with a total “horde” of 19075 authors. The main characteristic of average investigator is utilizing English as language of transmission of his/her knowledge (92.4%) and write alone (38% of manuscripts have a single author; mean 2.6 authors per paper, range 0-26; SD of 2). Moreover, the model researcher seems to look for journals published in English speaking countries. In fact, top five countries of publication are the US, the UK, Germany, Australia and Canada (85.8% English speaking, 2.9% German speaking). The trend is to choose traditional journals (or traditional plus e-publishing journals), instead of stand-alone e-publications (99.9% vs 0.1%). Authors show another clear tendency, i.e. to publish in their own homeland, or in a country with whom there is an historical of geographical cooperation (for example Canada and the US, India and England or Japan and Australia). Looking to the most prolific authors, the 1st one have published, during the considered interval period, 41 manuscripts creating his own CV starting moving on the “chessboard” of author’s position during years: 1st author in 10 manuscripts with 60% after 2001, and 2nd author in 23 manuscripts with 50% published after 2001. Further result on most cited authors will follow. Conclusion: CAN seems to be an hot topic for single-author: in fact the at-a-glance picture of the published manuscripts in the considered interval period strongly highlight the scarce frequency of corporate authorships, and much more than multidisciplinary teams. A number of reflections on most prolific authors and leading investigators working-in-the-field can be drawn. The study has pointed out that both being able to speak in English and coming form the US increase the probability to be able to publish a paper on CAN. Is it a choice of authors or a protectionist policy of publishers?
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.037 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".