chore(script): Update data description
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@ -490,10 +490,9 @@ Of these, {nr_relevant} have been identified as potentially relevant studies for
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<!-- {{++ FIXME: Update description for changing study pool ++}} -->
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<!-- {{++ FIXME: Update description for changing study pool ++}} -->
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The currently identified literature rises almost continuously in volume,
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The currently identified literature rises somewhat in volume over time,
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with small decreases between 2001 and 2008, as well as more significant ones in 2012 and 2016,
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with first larger outputs identified from 2014,
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as can be seen in @fig-publications-per-year.
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as can be seen in @fig-publications-per-year.
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Keeping in mind that these results are not yet screened for their full relevance to the topic at hand, so far only being *potentially* relevant in falling into the requirements of the search pattern, an increased results output does not necessarily mean a clearly rising amount of relevant literature.
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<!-- {{++ FIXME: give full year scale ++}} -->
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<!-- {{++ FIXME: give full year scale ++}} -->
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@ -520,14 +519,15 @@ df_study_years = None
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Anomalies such as the relatively significant dips in output in 2016 and 2012 become especially interesting against the strong later increase of output.
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Anomalies such as the relatively significant dips in output in 2016 and 2012 become especially interesting against the strong later increase of output.
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While this can mean a decreased interest or different focus points within academia during those time spans,
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While this can mean a decreased interest or different focus points within academia during those time spans,
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it may also point towards missing alternative term clusters that are newly arising, or a re-focus towards different interventions, and should thus be kept in mind for future scoping efforts.
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it may also point towards alternative term clusters that are newly arising, or a re-focus towards different interventions,
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and should thus be kept in mind for future scoping efforts.
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Looking at the distribution between white and gray literature a strong difference with white literature clearly overtaking gray literature can be seen, a gap which should not be surprising since our database query efforts are primarily aimed at finding the most current versions of white literature.
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The predominant amount of literature is based on white literature, with only a marginal amount solely published as gray literature.
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The gap will perhaps shrink once the snowballing process is fully completed,
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This represents a gap which seems reasonable and not surprising since the database query efforts were primarily aimed at finding the most current versions of white literature.
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though it should remain clearly visible during the entire scoping process as a sign of a well targeted identification step.
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Such a stark gap speaks to a well targeted identifaction procedure, with more up-to-date white literature correctly superseding potential previous publications.
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@fig-citations-per-year-avg shows the average number of citations for all studies published within an individual year.
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@fig-citations-per-year-avg shows the average number of citations for all studies published within an individual year.
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From the current un-screened literature sample, several patterns become visible:
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From the literature sample, several patterns emerge:
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First, in general, citation counts are slightly decreasing - as should generally be expected with newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.
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First, in general, citation counts are slightly decreasing - as should generally be expected with newer publications as less time has passed allowing either their contents be dissected and distributed or any repeat citations having taken place.
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```{python}
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```{python}
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@ -535,25 +535,28 @@ First, in general, citation counts are slightly decreasing - as should generally
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#| fig-cap: Average citations per year
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#| fig-cap: Average citations per year
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bib_df["zot_cited"] = bib_df["zot_cited"].dropna().astype("int")
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bib_df["zot_cited"] = bib_df["zot_cited"].dropna().astype("int")
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grpd = bib_df.groupby(["year"], as_index=False)["zot_cited"].mean()
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grpd = bib_df.groupby(["year"], as_index=False)["zot_cited"].mean()
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ax = sns.barplot(grpd, x="year", y="zot_cited")
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fig, ax = plt.subplots()
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ax.bar(grpd["year"], grpd["zot_cited"])
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sns.regplot(x=grpd["year"], y=grpd["zot_cited"], ax=ax)
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#ax = sns.lmplot(data=grpd, x="year", y="zot_cited", fit_reg=True)
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ax.tick_params(axis='x', rotation=45)
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ax.tick_params(axis='x', rotation=45)
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plt.tight_layout()
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plt.tight_layout()
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plt.show()
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plt.show()
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```
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```
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Second, while such a decrease is visible in relatively recent years (especially 2019--2023), it is not a linear decrease throughout but rather a more erratically stable citation output.
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Second, while such a decrease is visible the changes between individual years are more erratic due to strong changes from year to year.
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This points to, first, no decrease in academic interest in the topic over this period of time,
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This suggests, first, no overall decrease in academic interest in the topic over this period of time,
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second, no linearly developing concentration or centralization of knowledge output and dissemination,
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and second, no linearly developing concentration or centralization of knowledge output and dissemination,
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and third potentially no clear-cut increase of *relevant* output over time either.
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though it also throws into question a clear-cut increase of *relevant* output over time.
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Positive outlier years in citation amount can point to clusters of relevant literature feeding wider dissemination or cross-disciplinary interest, a possible sign of still somewhat unfocused research production which does not approach from a single coherent perspective yet.
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Positive outlier years in citation amount can point to clusters of relevant literature feeding wider dissemination or cross-disciplinary interest, a possible sign of still somewhat unfocused research production which does not approach from a single coherent perspective yet.
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It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the review process, a possible sign of more focused knowledge production and thus valuable to more closely review during the screening process.
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It can also point to a centralization of knowledge production, with studies feeding more intensely off each other during the review process, a possible sign of more focused knowledge production and thus valuable to more closely review during the screening process.
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Or it may mean that clearly influential studies have been produced during those years, a possibility which may be more relevant during the early years (2000-2008).
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It may also suggest that clearly influential studies have been produced during those years, a possibility which may be more relevant during years of more singular releases (such as 2011 and 2013).
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This is because, as @fig-publications-per-year showed, the overall output was nowhere near rich as in the following years, allowing single influential works to skew the visible means for those years.
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This is because, as @fig-publications-per-year showed, the overall output was nowhere near as rich as in the following years, allowing single influential works to skew the visible means for those years.
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In all of these cases, such outliers should provide clear points of interest during the screening process for possible re-evaluation of current term clusters for scoping.
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In all of these cases, such outliers should provide clear points of interest during the screening process for eventual re-evaluation of utilized scoping term clusters and for future research focus.
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Should they point towards gaps (or over-optimization) of sepcific areas of interest during those time-frames or more generally, they may provide an impetus for tweaking the identification query terms to better align with the prevailing literature output.
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Should they point towards gaps (or over-optimization) of specific areas of interest during those time-frames or more generally, they may provide an impetus for tweaking future identification queries to better align with the prevailing literature output.
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<!-- {{++ TODO: Add breakdown by thematic area++}} -->
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<!-- {{++ TODO: Add breakdown by thematic area++}} -->
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@ -588,6 +591,10 @@ plt.show()
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by_intervention = None
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by_intervention = None
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```
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```
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@fig-intervention-types shows the most often analysed interventions for the literature reviewed.
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Overall, there is a focus on measures of minimum wage and education interventions,
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as well as collective action, subsidies, trade liberalization changes and training.
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This points to a spread capturing both institutional, as well as structural and agency-driven programmes.
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<!-- {{++ TODO: describe intervention types with complete dataset ++}} -->
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<!-- {{++ TODO: describe intervention types with complete dataset ++}} -->
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