Acknowledgements are not just thank you notes: A qualitative analysis of acknowledgements content in scientific articles and reviews published in 2015
Bibliographic record
Abstract
Acknowledgements in scientific articles can be described as miscellaneous, their content ranging from pre-formulated financial disclosure statements to personal testimonies of gratitude. To improve understanding of the context and various uses of expressions found in acknowledgements, this study analyses their content qualitatively. The most frequent noun phrases from a Web of Science acknowledgements corpus were analysed to generate 13 categories. When 3,754 acknowledgement sentences were manually coded into the categories, three distinct axes emerged: the contributions, the disclaimers, and the authorial voice. Acknowledgements constitute a space where authors can detail the division of labour within collaborators of a research project. Results also show the importance of disclaimers as part of the current scholarly communication apparatus, an aspect which was not highlighted by previous analyses and typologies of acknowledgements. Alongside formal disclaimers and acknowledgements of various contributions, there seems to remain a need for a more personal space where the authors can speak for themselves, in their own name, on matters they judge worth mentioning.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.070 | 0.194 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".