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Chapter 2 Narrative Inquiry as Relational Multiperspectival Inquiry

2011· book-chapter· en· W2493645549 on OpenAlexaboutno aff
Janice Huber, Maureen Murphy, D. Jean Clandinin

Bibliographic record

VenueAdvances in research on teaching · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEducator Training and Historical Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsVisual artsNarrativePoint (geometry)ArtPsychologyLiteratureMathematics

Abstract

fetched live from OpenAlex

The children returned and Ms. Lee had them go to their desks. There was so much excitement in the air … . Ms. Lee has rearranged the desks again and I like how there are such frequent shifts in seating. Ms. Lee spoke of their photographs and their collages. She then said I would give the guiding question for their work on the citizenship education project today in their small sustained response groups. I fumbled badly and said something about who they are and how they belong. Ms. Lee wrote it on the board. As Ms. Lee continued to speak, I went and changed the words to “Who I am and how I belong.” Ms. Lee spoke to the children of how they were going to start putting their photos on their poster boards and to think about how their photographs were representations of who they were and where they belonged. No glue or scissors at this point. She also showed them the paper where she wanted them to write about their photographs.The children got their individual pieces of bristol board for their collages and Ms. Lee said they might want to choose a spot on the floor as they did this work. They were intent and focused on their own photographs but were also sharing with their neighbours. At one point, I commented to Ms. Lee, Simmee, and Jennifer about how impressed I was with their intentness. I spent some time with Logan who had some magnificent photographs … he has an eye for the aesthetic. I pointed out to him how much I liked the photographs. I also spent some time with Taylor who had three photographs of clothes: one Chinese outfit, one Korean outfit, and a long white dress that she said she did not know what it was. I asked if it was a christening dress and she said she thought so, that her mom had taken the photograph. She also had a close up of a Canadian flag. I spent some time with Sophie who had rejected some of her photographs as not interesting. When I pointed out what I saw as interesting things in her photographs, she started to see them more positively. I asked a few children what they planned to put in the centre of their collages. I realized, even as I asked that question, that I was privileging the centre photograph. Liam had his dad's photo clearly in the centre. He was busily writing words. He said he wasn't sure what to write about his dad but then wrote something about family being important. (Field notes, April 2, 2007)

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.534
GPT teacher head0.556
Teacher spread0.022 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations2
Published2011
Admission routes1
Has abstractyes

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