Between Survival and Growth… The Pandemic-Diaries and Human Development
Bibliographic record
Abstract
This paper presents an original study grounded in narrative and existential approaches to human development. The study aimed to identify strategies individuals used to balance the losses and gains experienced during the first wave of the COVID-19 pandemic, as represented in diaries. A repository of 60 personal documents underwent multifaceted semantic analysis, which examined content, form, and meaning-making levels. This analysis was guided by categories developed in two original Coding Sheets. As a result, two distinct types of diaries were identified: Survival Diaries (S-D), featuring references to pure growth and Growth Diaries (G-D). The G-D set was further divided into two subtypes: Compulsive Growth Diaries (G-D-C) and Texistence Growth Diaries (G-D-T). The primary distinction between authors of S-D and G-D lies in their experience of loss-waiting versus liberation-opening, which depends on the positive or negative evaluation of the pandemic and their openness to new, uncertain events. The more detailed components of this distinction include: (a) self-reflection; (b) narrative competencies; and (c) commitment to personal projects. The differentiation between G-D-C and G-D-T was established based on: (a) private concepts of development (media-mediated versus highly personalised); (b) ergodic persistence in deepened intertextual storying experiences; and (c) existential saturation of textualised experiences.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".