diff --git a/scoping_review.qmd b/scoping_review.qmd
index ec0918a..abd2cc1 100644
--- a/scoping_review.qmd
+++ b/scoping_review.qmd
@@ -28,14 +28,18 @@ zotero:
 ```{python}
 #| echo: false
 from pathlib import Path
-data_dir=Path("./02-data")
+DATA_DIR=Path("./02-data")
+BIB_PATH = DATA_DIR.joinpath("raw/01_wos-sample_2023-11-02")
 
 ## standard imports
 from IPython.core.display import Markdown as md
 import numpy as np
 import pandas as pd
+from matplotlib import pyplot as plt
 import seaborn as sns
 from tabulate import tabulate
+
+sns.set_style("whitegrid")
 ```
 
 # Introduction
@@ -280,7 +284,7 @@ Since scoping reviews allow both broad and in-depth analyses, they are the most
 import bibtexparser
 
 bib_string=""
-for partial_bib in data_dir.joinpath("raw/wos").glob("*.bib"):
+for partial_bib in BIB_PATH.glob("*.bib"):
     with open(partial_bib) as f:
         bib_string+="\n".join(f.readlines())
 sample = bibtexparser.parse_string(bib_string)
@@ -542,11 +546,81 @@ The results to be identified in the matrix include a study’s: i) key outcome m
 
 sample_size = len(sample.entries)
 md(f"""
-The exploratory execution of queries results in an initial sample of {sample_size} studies after the identification process.
-The majority of studies result from the ‘income’ inequality cluster of the Boolean search, with horizontal cluster terms used often but rarely on their own.
+The exploratory execution of queries results in an initial sample of {sample_size} potential studies after the identification process.
+This contains all identified studies without duplicate removal, controlling for literature that has been superseded or any other screening criteria.
 """)
 ```
 
+The currently identified literature rises almost continuously in volume,
+with small decreases between 2001 and 2008, as well as more significant ones in 2012 and 2016,
+as can be seen in @fig-publications-per-year.
+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.
+
+```{python}
+#| label: fig-publications-per-year
+#| fig-cap: Publications per year
+reformatted = []
+for e in sample.entries:
+    reformatted.append([e["Year"], e["Author"], e["Title"], e["Type"], e["Times-Cited"], e["Usage-Count-Since-2013"]])
+bib_df = pd.DataFrame(reformatted, columns = ["Year", "Author", "Title", "Type", "Cited", "Usage"])
+bib_df["Date"] = pd.to_datetime(bib_df["Year"], format="%Y")
+bib_df["Year"] = bib_df["Date"].dt.year
+
+# only keep newer entries
+bib_df = bib_df[bib_df["Year"] >= 2000]
+
+# create dummy category for white or gray lit type (based on 'article' appearing in type)
+bib_df["Type"].value_counts()
+bib_df["Literature"] = np.where(bib_df["Type"].str.contains("article", case=False, regex=False), "white", "gray")
+bib_df["Literature"] = bib_df["Literature"].astype("category")
+
+# plot by year, distinguished by literature type
+ax = sns.countplot(bib_df, x="Year", hue="Literature")
+ax.tick_params(axis='x', rotation=45)
+# ax.set_xlabel("")
+plt.tight_layout()
+plt.show()
+```
+
+Anomalies such as the relatively significant dips in output in 2016 and 2012 become especially interesting against the strong later increase of output.
+While this can mean a decreased interest or different focus points within academia during those time spans,
+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.
+
+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.
+The gap will perhaps shrink once the snowballing process is fully completed,
+though it should remain clearly visible during the entire scoping process as a sign of a well targeted identification step.
+
+@fig-citations-per-year-avg shows the average number of citations for all studies published within an individual year.
+From the current un-screened literature sample, several patterns become visible:
+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.
+
+```{python}
+#| label: fig-citations-per-year-avg
+#| fig-cap: Average citations per year
+bib_df["Cited"] = bib_df["Cited"].astype("int")
+grpd = bib_df.groupby(["Year"], as_index=False)["Cited"].mean()
+ax = sns.barplot(grpd, x="Year", y="Cited")
+ax.tick_params(axis='x', rotation=45)
+plt.tight_layout()
+plt.show()
+```
+
+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.
+This points to, first, no decrease in academic interest in the topic over this period of time,
+second, no linearly developing concentration or centralization of knowledge output and dissemination,
+and third potentially no clear-cut increase of *relevant* output over time either.
+
+Lastly, several years such as 2001, 2002, 2005 and 2008 are clear outliers in their large amount of average citations which can point to one of several things:
+
+It 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.
+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.
+
+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).
+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.
+
+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.
+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.
+
 # Synthesis of Evidence
 
 This section will present a synthesis of evidence from the scoping review.
@@ -644,7 +718,7 @@ TS=
             universal basic income OR
             provision of living wage OR
             maternity leave
-        ) 
+        )
         OR
         (
             cash benefits OR