Embracing the Perks of Faulty Roadmaps: A Literature Review of Sociological Perspectives on Budgeting<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This literature review provides an overview of sociological perspectives on budgeting. The organizational relevance of budgeting has made it the subject of many perspectives and comprehensive reviews. However, the “niche” of sociological budgeting research deserves more attention as it helps us understand how and why budgets thrive—not necessarily by providing direction, but precisely because they offer faulty roadmaps that evoke conflict, human interaction, individual reflection, power struggles, and change. The study reviews 115 publications concerned with these social dynamics of budgeting. The analysis is structured along Czarniawska‐Joerges and Jacobsson's (1989, Accounting, Organizations and Society 14(1), 29–39) discussion of three interrelated social functions of budgeting: a symbolic performance, a means of communication, and an expression of values. Based on this analysis, the study endorses sociological perspectives as a fruitful and often underrepresented resource to advance the discussion of the benefits and drawbacks of budgeting, identifying promising areas for future research that remain largely underexplored.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".