Psychoanalytical Approach to Management Research:
Bibliographic record
Abstract
Karl Popper has locked the reasoning of many researchers on a particular kind of rational thinking, that is, hypotheses stating and testing. For this reason, social sciences started to privilege a specific theory of personality. It is accurate to state that the common-sense knowledge, and resultant human "irrational" action, can be explained and even confronted by testing its assumptions. Nevertheless, Popper's categorization is not the only one possible. It neglects the irrationality of unconscious' intentions, a competing drive that directs human actions. In this paper, we discuss that, in accordance, management research and practice have strict relations with theories of personality that neglect the unconscious. For that reason, it assumes that humans are self-interested organisms like guinea pigs, neglecting this complementary supposition: the unconscious's intentions, structure, and dynamics that also drive human behavior, thinking, feeling, perceiving, and learning. The crucial integration of objective knowledge with the unconscious dynamic supposes the addition of the psychoanalytical problem to Popperian's psychological problem. Thus, the derivate capacity to explain human and social action understood as intention, plan, and act must consider conscious and unconscious intentions. The psychoanalytical approach to management research also provides ingenious methods like the awareness-enhancing interviews we present.
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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 | 0.022 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.055 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| 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".