Understanding and studying value as a duality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To cope successfully with the pressures imposed by a devastating pandemic, social inequities and climate change, companies and policy makers need to take a hard look at how they conceptualize, define, measure, and operationalize “value”. Our intent in this paper is to support this conversation. We make the case that “value” is an ill-defined and complex construct, the conceptualization of which has perplexed intellectuals from the beginning of Western civilization to now, arguing that the construct meets none of the characteristics of conceptual clarity outlined by Suddaby (2010). We present a historical overview 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 (value depends on the personal response of the observer to what is being considered) views over time. With this context in mind, we outline 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 either subjective or objective, we recommend that scholars consider value’s objectivity and subjectivity to be interrelated and complementary and make use of both quantitative and qualitative methodologies in studies of this construct. The more nuanced understanding of value provided in this paper should assist those who are interested in examining the worth of investments that have observable expenses but less quantifiable outputs.
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.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it