Negative aspects of counter-knowledge on absorptive capacity and human capital
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
Purpose – People live and work in a world where they do not have complete knowledge and, as a result, they make use of rumours, beliefs and assumptions about relevant areas of concern. The term counter-knowledge has been used to refer to knowledge created from unverified sources. The purpose of this paper is to examine the relationship between counter-knowledge and human capital (HC) as well as investigating interactions between absorptive capacity (ACAP) and HC. Design/methodology/approach – A model is tested to examine the relationship between counter-knowledge, HC and the financial performance of 112 companies listed on the Spanish Stock Exchange. Findings – The results are calculated using structural equation modelling. This leads to the main conclusion that while the increasing presence of counter-knowledge leads to a reduction of ACAP and, by extension with HC. However, in the context of the sample, HC has positive effects on firms’ performance. Therefore, consideration must be given to the evaluation of the real cost of counter-knowledge or inappropriate assumptions on HC. Practical implications – The key managerial implication of this paper is that management should actively develop an organizational culture which questions the source of any knowledge and favours evidence-based reasoning over reasoning based on “gut instinct”, what has worked in the past and reasoning based on rumours and gossip. Originality/value – This paper provides empirical support for the argument that the all so-called “knowledge” generated from the sharing of unverified news is not necessarily good knowledge. Rumours or gossip shared thanks to unverified sources are some examples that illustrate people possibility to create inappropriate or false beliefs via unsupported explanations and justifications.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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