Creating Investors, Not Tourists: How to Care for the Linguistic Ecosystem.
Bibliographic record
Abstract
The role of the facilitator within Communities of Philosophical Inquiry (CPI's) has often been allocated to structuring group interactions and/or affirming participants' contributions. In this paper, however, it will be argued that facilitators must take a far more active role in dialogue than has hereto been recognized. This is the case because, when left to its own devices, CPI dialogue often devolves into mere opinion tourism, becomes obscure, and/or is drowned by an excess of irrelevant content. It will be argued that these effects, in turn, pose a serious threat to agent investment. That is, by muddying dialogue, these effects can sever the link between agents' motivational sets and the subject matter at hand and, consequently, may cause agents to internally disengage from the discussion underway. \n\tGiven the danger that unchecked dialogue poses to agent investment, it will be argued that facilitators must be vigilant in attending to the health of the linguistic environment that both they themselves and participants occupy. That is, it will be argued that facilitators have a responsibility to care for participants by intervening in dialogue and pushing for rigour and clarity. \n\tThis ecocentric model of care, interestingly, often directly contends with the more intuitive, or biocentric position, that a facilitator must directly care for participants affective or emotional welfare by celebrating their contributions for contribution's sake. Instead, it suggests that facilitators can indirectly care for participants by strategically prompting them to make their contributions logically sound, concise and clear. Moreover, this ecocentric perspective also conflicts with the often purported view that a facilitator is a temporary figure that should eventually become obsolete in a CPI. To the contrary, the ecocentric perspective suggests that a facilitator's role is indispensable to a CPI's success, insofar as it helps create and maintain the necessary conditions for agent investment, and helps ensure the continued health of the linguistic ecosystem, upon which everybody's welfare crucially depends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".