Bloggers on FIRE Performing Identity and Building Community: Considerations for Cyber-Autoethnography
Bibliographic record
Abstract
As a research approach, autoethnography has revolutionized qualitative inquiry. To date, most autoethnographies represent the lives of academics and are published in the research press for a small audience of other academics. However, in the digital world, a subset of blogs has emerged in which the self-narratives are substantially similar to autoethnographies in content, quality, and level of social commentary, but with a broader scope and audience. For example, FIRE bloggers write about how they are striving to reach the goal of Financial Independence and Early Retirement (FIRE). They share detailed accounts of their financial circumstances, personal stories, strategies, and social insights. Through an analysis of FIRE blog texts, I examine digital presentation and performance of identity, relational aspects of online communication, and strategies these bloggers and their followers use to create community. The success of bloggers in bringing together people around the world to form communities with shared aims points to possibilities for how cyber-autoethnographers might broaden the reach of autoethnography while also building a collaborative sense of agency to accomplish personal and political goals. My interest in this cyber-community is theoretical, but intersects with challenges I have grappled with in my personal transition to retirement.
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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 | 0.132 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".