Choice and control: corpus-based discourse analysis of teacher education policy in England (2010-2021)

Figures & data

Figure 1. Keywords identified in the MCEP corpus.

Output from Wordsmith 8 software (Scott, 2020)

Table 1. Collocate clusters of keyword “ITT”

Cluster	Frequency
ITT Core Content	42
The ITT Core Content	35
Framework for ITT content	21
A Framework for ITT	18
Initial Teacher Training ITT	13
of a framework for	11

Table 2. Collocates to keyword “ITT”

Collocate	Frequency		Dice	MI	MI3	Z	Log_L	T	Log_R
CONTENT	122		0.72	14.38	26.17	509.91	1743.12	11.04	−3.41
CORE	64		0.57	14.11	24.96	335.82	867.83	8	−2.76
FRAMEWORK	77		0.21	12.5	23.63	210.89	857.03	8.77	−4.63
SHOULD	85		0.04	10.04	21.3	93.71	651.81	3.46	−4.54
INITIAL	25		0.07	10.99	20.27	70.86	219	5	−4.52
SYSTEM	26		0.05	10.46	19.86	60.05	208.61	5.1	−5.11
DEVELOPMENT	21		0.03	9.78	18.56	42.47	148.3	4.58	−5.48
COURSE	20		0.03	9.59	18.23	38.77	135.97	4.47	−5.6
NEW	22		0.02	9.01	17.93	33.01	132.08	4.68	−6.32
SUBJECT	20		0.02	8.82	17.47	29.47	114.97	4.46	−6.36
TEACHER	21		0.01	7.38	16.16	17.61	80.06	4.56	−7.88

Note: Parameters: 03=MI(3), L5-R5, C100-NC100

Figure 2. Concordance example keyword “ITT” and “should” in the MCEP corpus.

Output from Wordsmith 8 software (Scott, 2020)

Table 3. Collocate clusters of keyword “Trainee”

Cluster	Frequency
Trainee Teachers	15

Figure 3. Concordance example keyword “Trainee(s)” in the MCEP corpus.

Output from Wordsmith 8 software (Scott, 2020)

Figure 4. Concordance example “Trainee(s)” in the EP Corpus.

Output from Wordsmith 8 software (Scott, 2020)

Scott, M. (2020). WordSmith Tools version 8. Lexical Analysis Software.

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