Bibliographic record
Abstract
This chapter helps therapists to integrate and consolidate their capacities for shaping Stage 1 change. Using the metaphor of the Inuit people’s human-shaped stone markers, called inukshuks or inuksuit , to guide them in travel across the vast arctic landscape, this chapter presents a series of markers to situate clients on the terrain of therapeutic change in Stage 1 of EFT, together with therapist questions in search of these markers. The chapter focuses on spotting and following client markers, helping the reader to integrate and consolidate what it means to be an EFT process consultant dancing the EFT Tango in attunement with clients as they move through Stage 1. The chapter reviews signposts or markers that situate clients on Stage 1 steps of client change and describes how to follow these markers with the EFT Tango. Markers and therapist questions in search of these markers begin with identifying racial, ethnic, and cultural (REC) doorways, important for alliance building and empathic understanding. This is followed by a search for markers of typical positions and moves or strategies for engagement; markers of core underlying emotion; markers that stabilization is in progress and finally markers that stabilization/de-escalation has been achieved.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".