The Manager and Love: Evoking a Loving Inquiry in a Group Setting
Bibliographic record
Abstract
Abstract Neuroscientists, psychologists, educators, and management scholars propose that the current emphasis on intellect and reason in education and business over values such as love, connectedness, and compassion are at the root of many business ethical failures and societal problems. They argue not that reason should be abandoned in education and business management but rather that it needs to be balanced with values such as love because these attributes are innately human, enabling wise decision-making. This is a difficult task in the context of the current ethos of intellect and reason that dominates education and management. To correct the imbalance, we must explore ways of preparing future managers to accept the relevance and importance of learning to develop and embody love. Through our research, we provide an experience of community love by creating a caring, receptive, personal container. We engaged in the practice of Collaborative Autoethnography, integrating the Nguni South African concept of Ubuntu, to explore, research, and demonstrate the experience of love in a community setting. To support this practice, we framed it against the background of integrative justice, focusing on authentic engagement without exploitative intent as per Santos and Laczniak’s (2015) Integrative Justice Model (IJM) and built upon some common contexts from which love is considered such as Catholic Social Thought (CST) and indigenous cultures. We analyzed why and how love might be implemented in education and management and how Collaborative Autoethnography can be applied in connecting with others to research, learn from, and build upon the experience of love and connectedness.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.026 | 0.054 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".