The roots and development of constructivist grounded theory
Bibliographic record
Abstract
AIM: To deconstruct how Charmaz's constructivist grounded theory (CGT) evolved from the original ideas of Glaser and Strauss, and to explore how CGT is similar to and different from the original grounded theory (GT). BACKGROUND: The origins of GT date to 1967 with Glaser and Strauss's study of the treatment of dying individuals, applying an inductive method allowing for the development of theory without the guidance of a preconceived theory. CGT moves away from the positivism of the Glaserian and Straussian GT schools, approaching GT through a constructivist lens that addresses how realities are made. DATA SOURCES: This article does not involve the collection and analysis of primary data; instead, academic literature written by leaders in the field of GT was reviewed to generate the ideas presented. REVIEW METHODS: Comprehensive literature review drawing on the 'integrative review' principles. DISCUSSION: When selecting a GT approach, the possibility of a congruence between the chosen methodology and the worldviews of the researcher's discipline and own outlook should be considered. CONCLUSION: The differences among the various schools of GT lie in their overarching goals and their perspectives of the nature of reality. IMPLICATIONS FOR RESEARCH/PRACTICE: Considering the alignment between the constructivist worldview and the field of nursing, CGT offers a valuable methodology for researchers in this area.
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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 | 0.094 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".