One-Sentence, One-Word: An Innovative Data Collection Method to Enhance Exploration of the Lived Experiences
Bibliographic record
Abstract
Experienced-based methods are growing in popularity and are increasingly being utilized in a variety of research programs and investigations. They enable researchers and participants to co-design research strategies and outcomes and subsequently propose solutions to potential problems in the partnership. By applying an experience-based methods lens, we sought to augment traditional qualitative interviewing methodologies by using a novel method we named “one-sentence, one-word” (1S1W). To apply our 1S1W method, we used a phenomenological study that examined the relationship between the risk of falling and the desire of master athletes to engage in competitive sports. Participants reflected and recorded their subjective experiences in the form of one-sentence and one-word responses, at the beginning and end of the interview, respectively. Half of all participants associated the risk of falls with negative words; however, all participants used positive sentences to describe their experience as master athletes. Considering other qualitative findings, this method, while brief and relatively simple, gave a very rich and accurate depiction of participants’ overall experiences (e.g., themes). The 1S1W data collection method complements traditional qualitative approaches and encourages participant reflection; we believe our method has applicability across the research process. In one word, it isolates the ESSENCE.
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 | 0.045 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".