Community EQ: Empathy as a KPI in Post-COVID19 Business Culture
Bibliographic record
Abstract
This white paper proposes that community empathy—in other words, actively practicing collective empathy—be a core element of the future of work in all business organizations, and that it be embraced actively as a key performance indicator using socio-technological solutions. First of all, we present the current state of work as it intersects with (post)COVID19 society, drawing attention to the urgency and recognized need for nurturing “soft” human capabilities like empathy among all employees. We then dive deep into the concept of empathy as it is being harnessed and monetized around the world for the betterment of business and society at large, and argue for a capabilities approach to working with empathy. We also critically reflect on the methods that have been used to create community within business organisations, and share insights into the human science behind community building, and collective thinking and feeling. The solution we propose is to co-create spaces that are conducive to nurturing the practice of empathy. This is a multidimensional process which involves having a good understanding of what empathy actually is, curation, preparation, and the mindful matching of people. In addition to expanding on these dimensions, we provide tips for designing the settings for the encounter(s), and emphasize the importance of reflection and feedback, and the crucial role of consistency and repetition in habit formation. The paper concludes with a succinct overview of the outcomes we as human beings, as employees, as employers, as family members, as citizens and denizens can expect to enjoy as our empathetic capabilities increase - not only at work but also in all of our social relations and interactions.
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.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.023 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".