Bibliographic record
Abstract
Purpose To cope successfully with the pressures imposed by a devastating pandemic and other challenges, companies and policymakers need to look at how they conceptualize, define, measure and operationalize “value”. This paper aims to support this conversation. Design/methodology/approach This study presents a historical review of how the value construct has been conceptualized over time, demonstrating that its history is one of tension and debate with conceptualizations swinging between objective (i.e. the value of something exists independent of the observers) and subjective (i.e. the value of something depends on the personal response of the observer to what is being considered) views over time. Findings This paper outlines the implications to researchers of value’s low construct clarity, offering suggestions designed to exploit rather than ignore the duality of the value construct. Instead of thinking of the value construct as being subjective or objective, this study recommends that scholars consider value’s objectivity and subjectivity as being interrelated and complementary. The paper recommends that researchers use both quantitative and qualitative methodologies in studying this construct. Research limitations/implications A major limitation of this paper is the word count limitation restricting the extent to which this paper could explore a more comprehensive list of the conceptualizations of value throughout history. Practical implications This paper presents practitioners with a nuanced understanding of value that should assist those interested in examining the worth of investments with observable expenses but less quantifiable outputs. Originality/value The authors have not found a similar analysis of the various conceptualizations of value.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.052 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".