The Humanbecoming theory as a reinterpretation of the symbolic interactionism: a critique of its specific nature and scientific underpinnings
Bibliographic record
Abstract
Discussions about real knowledge contained in grand theories and models seem to remain an active quest in the academic sphere. The most fervent of these defendants is Rosemarie Parse with her Humanbecoming School of Thought (1981, 1998). This article first highlights the similarities between Parse's theory and Blumer's symbolic interactionism (1969). This comparison will act as a counterargument to Parse's assertions that her theory is original 'nursing' material. Standing on the contemporary philosophy of science, the very possibility for discovering specific nursing knowledge will be questioned. Second, Parse's scientific assumptions will be thoroughly addressed and contrasted with Blumer's more moderate view of knowledge. It will lead to recognize that the valorization of the social nature of existence and reality does not necessarily induce requirements and methods such as those proposed by Parse. According to Blumer's point of view, her perspective may not even be desirable. Recommendations will be raised about the necessity for a distanced relationship to knowledge, being the key to the pursuit of its improvement, not its circular contemplation.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.129 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 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".