Founding concepts and foundational work: Establishing the framework for the use of acknowledgments as indicators
Bibliographic record
Abstract
Building on the concepts of the reward system of science and social capital, Blaise Cronin brought forth the idea that rewards in science are threefold, forming a triangle built from authorship, citations, and acknowledgements. Of these, acknowledgments are the hardest to grasp and evaluate. After nearly 45 years of multidisciplinary research on acknowledgments and a corpus of over 80 scientific contributions, there is still no consensus on the value of acknowledgments in scholarly communication. This study aims to further acknowledgments research with a meta-synthesis of the literature, establishing the theoretical framework for the use of acknowledgments as bibliometric indicators. Based on in-progress content analyses, broad categories emerge revealing contextual information crucial to the understanding of acknowledgments. Applying our framework on data from the Web of Science, further phases of this study will provide large-scale findings based on a multidisciplinary sample. From there, it will be possible to envision recommendations for the standardization and use of acknowledgments as indicators. However, grounding the study of acknowledgments in their underlying theoretical considerations and conceptual foundations will ensure these recommendations respect the diverse traditions of the scientific field. Conference Topic Theory Introduction and background It is a broadly recognized fact that the scientific field has a very “high degree of codification”, to borrow the Bourdieusian phrase (Bourdieu, 1996, p. 226). How and when one is admitted into the academic community, how a researcher acquires credibility within the scientific realm, and what contributions turn a researcher into a renowned scholar are endlessly evaluated, measured, and scrutinized. This high degree of codification helps to both foster and assuage the paradox that underlies the use of empirical measures to define what remains an intrinsically nuanced and contextualized concept: scientific “success”. Merton (1973) presented the sociology of science with the reward system of science, its recognition paradigm, and the nepotistic undertones of the Matthew effect; Bourdieu reframed the concept of recognition to befit the concept of symbolic capital. Blaise Cronin brought forth the idea that these rewards are threefold, forming a triangle built from authorship, citations, and acknowledgements (Cronin, 1995; Cronin, 2005; Cronin & Weaver-Wozniak, 1993). These are all part of the illusio, which encompasses the stakes of the academic “game”, its rules, and the very fact that its rewards are worth pursuing (Bourdieu, 1988, p. 56). Of these rewards, acknowledgments are the hardest to grasp and evaluate; reasons range from lack of standardization to name-dropping and ambiguous wording (Cronin, 1995; Cronin, 2014), as well as the placement of acknowledgments, which can vary from in-text mentions to paratextual elements situated outside the body of the text (Genette, 1997). Researchers have also called for stricter policies to inform the use of acknowledgments, prescribe their form, offer conditions for inclusion, or establish their ethical ramifications (Brown, 2009; Chubin, 1975; Pontille, 2001). For example, while Cronin’s research (Cronin, 1995) showed that in
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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 | 0.168 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.041 | 0.028 |
| Science and technology studies | 0.011 | 0.089 |
| Scholarly communication | 0.024 | 0.050 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".