Volunteer Experiences on Organic Farms: A Phenomenological Exploration
Bibliographic record
Abstract
This article presents an exploration of the understudied phenomenon of volunteering on organic farms, a movement associated with World Wide Opportunities on Organic Farms (WWOOF). Using a hermeneutic phenomenological lens influenced by philosophies of Hans George Gadamer, this article illuminates experiences of volunteers on organic farms in Argentina, experiences we denote as “organic volunteering.” Our use of phenomenology provides an opportunity to develop deeper understandings of these lived experiences and what they mean to volunteers. Data collection and analysis of active interviews and participant observation with volunteers revealed a central understanding of opening to living in interconnectedness, which is underpinned by six horizons of understanding: 1) reconnecting, 2) exchanging knowledge, 3) experiencing harmony, 4) bonding with others, 5) consciousness raising, and 6) transforming. Our work suggests that while these experiences are likely similar to volunteer or even alternative tourism broadly defined, organic volunteering encompasses aspects that may extend beyond what has been put forward by volunteer tourism researchers, and is perhaps its own niche of alternative tourism.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".